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R.V. College of Engineering


         R.V. COLLEGE OF ENGINEERING, BANGALORE-560059
         (Autonomous Institution Affiliated to VTU, Belgaum)




Reliability Study and Analysis on Open Source Enterprise Resource
                    Planning Software Package
                                      MINI PROJECT REPORT

                                              Submitted by
                         Mayank Baheti                         1RV09IM024
                         Deepak Rathod                         1RV09IM009
                         Suraj Soni                            1RV09IM044
                         Tanay Agrawal                         1RV09IM046



                                       Under the Guidance of

                                     Mr. Vikram Bahadurdesai
                                           Asst. Professor,
                                          IEM Department,
                                    R.V. College of Engineering,
                                         Bangalore 560059.




      In Partial fulfilment of the academic requirements of VI Semester B.E

                Programme in Industrial Engineering and Management

                                                 2011-12


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         Sl.No            Topic                                         Page. No


                          Abstract                                          i.


           1.             Introduction                                     7


                1.1.      Overview to Software Reliability                 7


                  1.2     Overview of Open Source ERP Software             7

                  1.3.    Principles of open source architecture           8

                          Introduction to open source architecture         8
1.3.1.


                          Overview Apache open for business (Ofbiz)        9
         1.3.2.


                   1.3.3. Features of Apache open for business(Ofbiz)      10


                   1.3.4. Different Open Source ERP Software’s             10

                1.4.      Literature Review                                13


                1.5.      Problem Genesis                                  15


                1.6.      Objectives                                       16


           2.             Theoretical Concepts and fundamentals            17

                2.1.      Software reliability modeling                    18



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               2.2.       Software reliability modeling classification         19

               2.3.       Theoretical Comparison of Techniques                 21

               2.4.       Exponential Model                                    23


          3.              Methodology                                          25


               3.1.       Study of Ofbiz ERP software package                  25


               3.2.       Develop Operational Profile                          25


               3.3.       Testing                                              25


                          Performance testing on different operating systems   25
3.3.1.


                          Performance testing on different web browsers        26
3.3.2.


               3.4.       Data Collection                                      28


                 3.4.1. Performance testing of different modules of Ofbiz on
                          different Web browsers.
                                                                               29


                 3.4.2. Functionality Testing                                  35


                 3.4.3. Test cases                                             36


                 3.4.4. Ofbiz Defect Tracker                                   39




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              3.5.       Data analysis                                 46


         4.              Conclusions                                   56


         5.              Future Scope                                  57


                         References




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                                              Abstract
      Software reliability turns out to be most important part of business today,
which plays an important role in assuring the quality of software systems. Software
reliability is built in a system by performing exhaustive testing. Testing is an
important phase in software development lifecycle and is generally considered to
be costly and tedious. But it is very much essential to ensure the quality of the
product. The ideal goal of most of the companies is to develop robust software by
testing it cost-effectively. Robustness should be inherently developed along with
product development rather than incorporating it after the implementation phase.
The idea is to test early and to test often.
    Software reliability is the probability of failure free software operation for a
specified period of time in a specified environment. The high complexity of the
software is the major contributing factor of software reliability problems. Software
reliability is also important factor affecting system reliability. Software reliability
is an important attribute of software quality, together with functionality, usability,
performance, serviceability, capability, install ability, maintainability and
documentation. Software reliability engineering (SRE) is an emerging discipline,
SRE is the application of statistical techniques to specify, predict, estimate and
assess the reliability of software based systems.
    Testing open source apache Ofbiz ERP software gives as opportunity to have
inferences such as reliability, mean time to failure (MTTF) etc, for software system
and gain its credibility in this field. Software Testing is process of executing a
program with the intent of finding error. A good test case is one that uncovers an as
yet undiscovered error. Testing should systematically uncover different classes of
errors in a minimum amount of time and with a minimum amount of effort.
Generating test sequences from usage probability distributions, assessing statistical
inferences based on analytical results associated with test process and also derive

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stopping criterion of test process. But the problem associated in this process is to
test the software behavior in different types of environment and measuring the
reliability.
    The purpose of testing can be quality assurance, verification and validation, or
reliability estimation. Software testing is a trade-off between budget, time and
quality. To predict reliability of software failure data need to be collected during
testing phase of the software developmental life cycle. So software testing plays a
crucial role in estimating the reliability of software system. In this work, Ofbiz
ERP software package is tested for its performance on different operating systems
and on different web browsers. Finally functionality test has been conducted by
generating test cases. This result in identifying different bugs, using work around
methods bugs has been fixed. Our experimental efforts lead us to a more practical
and effective approach for software reliability.




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                                  1.0 INTRODUCTION
1.1 Overview of Software Reliability
Software reliability is one of the major software quality attributes, which quantitatively expresses
the continuity of correct service delivery. Reliability models are typically measurement based
models, and mostly employed in isolation at the later stage of the software development process.
In current practice, early software reliability prediction models are often insufficiently formal to
be analyzable and not usually connected to the target system. Additionally, despite the vast work
that has been done in software reliability, much work is still needed, especially in the
component-based development arena regarding availability of software component reliability
information following a clear failure classification scheme. Aiming at addressing these problems,
this work contributes a novel reliability prediction technique that leverages reliability analysis in
early stages of software development by taking into account, the component structure exhibited
in the scenarios elicited in the requirements phase and the concurrent nature of component-based
systems. Following that contribution, this thesis proposes a means to accomplish reliability
design and analysis for model driven engineering following the Model Driven Architecture
standards. By doing that, this research work contributes to the task of systematically integrating
reliability modeling from the early to the late stages of software engineering and thus
semantically integrating analysis, design and deployment models for reliability into one
environment. Open Source Software adoption in large companies is considered to be a relatively
recent movement. Open Source Software is gaining terrain in large organizations, some see it as
just another development alternative; others see in it a strategic competitive advantage. In spite
of those interests and efforts, techniques available to validate a design against nonfunctional
properties, particularly reliability, often require significant expertise unrelated to the usual
business of engineering software. As reliability measures quantitatively the quality of correct
service delivery, it is probably the most important characteristic for the software engineering
discipline

1.2 Overview of Enterprise Resource Planning (ERP)
An ERP system is fully integrated business management system covering function area of an
enterprise like logistics, finance, accounting, production and human resource. It organize and
integrates operation process and information flow to make an optimum use of resources such as


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men, material, money and machine. ERP is global, tightly integrated close loop business solution
package and it’s multifaceted.
ERP promises one database, one application and one user interface for entire enterprise, where
once disparate system ruled, manufacturing, distribution, finance and sales. Taking information
from every function, it is a tool that assists employee and manager plan, monitor and control the
entire business. A modern ERP system enhance a manufacturing ability to accurately schedule
production, fully utilize capacity, reduce inventory and meet promised shipping dates.




                                       Fig1.1:- General Model of ERP



1.3 Principles of Open Source Architecture
1.3.1 Introduction
Open Source Software (OSS) in general refers to any software whose source code is freely
available for distribution. The success and benefits of OSS can be attributed to many factors such
as code modification by any party as the needs arise, promotion of software reliability and
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quality due to peer review and collaboration among many volunteer programmers from different
organizations, and the fact that the knowledge-base is not bound to a particular organization,
which allows for faster development and the likelihood of the software to be available for
different platforms.
Characteristics of OSS: It is generally acquired freely. Manufacturer or developer has no right
to claim royalties on the distribution or use. Source code is accessible to the user and distributed
with the software. No denial to an individual or to a group to access source code of the software.
It has provision of modifications and derivations under the programme’s original name. Rights of
facilities attached to the programme must not depend on the programme’s being part of a
particular software distribution. Licensed software can not place restriction on other software
that is distributed with it. Distribution of License should not be specific to a product and License
should be technology neutral, etc.
1.3.2 Overview of Apache Open for Business (OFBiz)
Apache OFBiz (The Apache Open For Business Project) is a community-driven open source
project. For many organizations, OFBiz is also the best e-commerce and enterprise resource
planning (ERP) software available.
1. No Licensing Fees: OFBiz is free and open source.
2. Credibility: OFBiz users can rely on the organizational, legal, and financial stability that
    comes with OFBiz being a top-level project at the Apache Software Foundation (ASF).
3. Collaboration: OFBiz is licensed under the Apache 2.0 open source license, which is both
    open and business-friendly, facilitating community-driven, meritocratic collaboration while
    allowing proprietary derivative applications.
4. Flexibility: Users will have complete access to source code it will eliminate "proprietary
    system" limitations. The entire open source community benefits from making OFBiz as clear,
    flexible, and reusable as possible.
5. Lower Cost: OFBiz can help user to achieve a system that is as good or better than those
    available from major proprietary ERP vendors at a significantly lower total project cost. With
    OFBiz, user can budget for custom features and added value rather than license and
    maintenance fees.
6. Scalability: Based on the Java platform, OFBiz has the capacity to scale dramatically as
    needed.


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7. Third Party Friendly: Enjoy freedom from indefinite vendor lock; with OFBiz, user can fully
    leverage internal resources and/or any of the dozens of organizations offering quality OFBiz
    implementation and support services.
8. Frequent Updates: Benefit from the active and ongoing contributions of the world-wide
    OFBiz community.
9. Feature Rich: Leverage standards-based tools and components are attractive to user
    technology staff, yet already integrated into a common framework.
10. Expert Leadership and Support: HotWax Media provides system design, project planning and
    management, along with robust technical muscle to deliver the implementation and support
    necessary for user’s business.


1.3.3 Features of Apache Open for Business (OFBiz)
Apache OFBiz applications and brief descriptions
                                       Table 1.1: OFBiz applications
 Accounting              Setup your chart of accounts, manage agreements, billing, invoices,
 Manager                 payments, and more.
 Catalog Manager         Create catalogs and populate products by categories. Maintain product
                         features, price rules, promotions, subscriptions, reviews, and more.
 Content Manager         Underlying CMS capability that can be customized to manage website
                         content, blogs, surveys, and more.
 Facility Manager        Pick, pack, and ship while maintaining inventory information.

 Manufacturing           MRP, job shop, routing and routing task screens, and BOM screens.
 Manager
 Marketing               Maintaining mailing lists, manage online marketing campaigns that are
 Manager                 fully integrated with e-commerce.
 Order Manager           Manage purchase and sales orders, create orders, handle returns
 Party Manager           Create individuals and groups, manage roles.
 Web tools               Track site traffic and related performance metrics.
 Application
 Work Effort             Events, calendar, project management, and more ready to be customized
 Manager                 to meet your specific needs.


1.3.4 Different Open Source ERP Software’s
Enterprise Resource Planning (ERP) is most crucial for a business and they really help business
to streamline multisite environment with unified processes across locations. There are so many


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proprietary and commercial ERP solutions available like, SAP, Microsoft Dynamics, Oracle e-
Business etc. But, very few know that there is a plethora of Free or Low Cost Open Source
solutions that are extremely competent as well as with proper customer support just like the
proprietary ones. Here is a list of 12 such Open Source ERP/CRM solutions that are either cross
platform or browser/web based so that the solutions run on any platforms. All of them have Free
versions available without support and some of them offer paid support as optional.
    a. Compiere: Compiere is a Comprehensive, Adaptable and Low Cost ERP (Java based)
        solution. Over 1.35 million downloads indicate the proven ERP package. Compiere has
        support for Cloud Infrastructure, Integration with Sales force and much more. Automate
        all your business services functions, improve efficiency and customer satisfaction, easy
        quick customizations, high quality but with low cost ERP suit that will give other
        competitors a run.
    b. PostBooks: PostBooks is a full-featured, fully-integrated accounting, ERP, and CRM
        system, based on the award winning xTuple ERP Suite. Built with the open source
        PostgreSQL database and the open source Qt framework for C++, it provides the ultimate
        in power and flexibility for a range of businesses and industries.
    c. Open taps: Open taps is a full-featured ERP and CRM suite which incorporates several
        open source projects, including Apache Geronimo, Tomcat, and OFBiz for the data
        model and transaction framework; Pentaho and Jasper Reports for business intelligence;
        Funambol for mobile device and Outlook integration; and the Open taps applications
        which provide user-driven applications for CRM, accounting and finance, warehouse and
        manufacturing, and purchasing and supply chain management.
    d. Adempiere: A Java based ERP-System which started as a fork of Compiere, supports a
        lot of features. A fully fledged ERP, CRM and Supply chain management, Point of Sale
        suit.
    e. WebERP: webERP is a complete web based accounting/ERP system that requires only a
        web-browser and pdf reader to use. It has a wide range of features suitable for many
        businesses particularly distributed businesses in wholesale and distribution. It is
        developed as an open-source application and is available as a free download to use. The
        feature set is continually expanding as new businesses and developers adopt it.




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    f. BlueERP: BlueERP is a double entry accounting application for small and medium
        business. Written in PHP, it is delivered through a LAMP environment to provide web
        access to your accounts.
    g. Dolibarr: Dolibarr is an ERP/CRM for small and medium companies but also
        independent or foundations. Dolibarr success is probably due to the 3 simple rules
        applied on project since the beginning: Easy to install, Easy to use, Easy to develop.
        Supports lot of features like Supply chain management, proposal management, order
        management, payment management and much more.
    h. ERP5: A full featured high end open source ERP designed for better business process,
        collaboration and leaner management.

    i. JFire: JFire is a new, powerful and free ERP, CRM, eBusiness and SCM /SRM solution
        for business enterprises. JFire is entirely free/open-source software, uses the latest Java
        technologies (EJB 3, JDO 2, Eclipse RCP 3.3) and is designed to be highly customizable.
        It is a complete and extensible solution that fulfills all your business needs like user
        management, online trade with business partners, points of sale, various distribution
        channels forming a distribution network, store management etc.
    j. OpenERP: OpenERP is open source ERP suit that supports Enterprise modules,
        Logistics, Accounting and Finance, HRM, CRM, Project Management, Business Process
        and more. It is complete package with commercial version available.
    k. Apache OFBiz: The Apache Open for Business Project is an open source enterprise
        automation software project licensed under the Apache License Version 2.0. As per
        OFBiz, Open Source enterprise automation means: Open Source ERP, Open Source
        CRM, Open Source E-Business / E-Commerce, Open Source SCM, Open Source MRP,
        Open Source CMMS/EAM, and so on.
    l. OpenBravo: OpenBravo is a web based ERP solution originally was based on Compeiere
        which is also Open Source. It supports standard ERP features like production
        information, inventory, customer information, order tracking, and workflow information.




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1.4. Literature Review
Parijat Upadhyay et al. made an effort to explore and elaborate the issues in the implementation
and reliability of ERP software small and medium scale enterprise (SMEs) with the help of
Pareto Analysis since the functionality used at SMEs is not the same as in large scales, hence
sacrificing the modules is the secret behind successful implementation of ERP and hence the
reliability. [1]


Poonam Garg et.al. explains the reasons for failure in implementation of ERP packages in Indian
Retail Organizations. The major roadblocks in the implementation of ERP are inadequate
resources and poor involvement of end user. ERP is more of a people project then an IT project
which cannot be reliably implemented unless there is proper involvement and input by the end
user. [2]


Mark C. Van Pul et.al. have provided a general overview of theories and processes that is
followed to check the reliabilities of a software package. With the help of intensive case studies
and mathematical formulas of reliability, it comprehends the fact that no software is complete
and even a zillion dollar satellite has bugs. [3]


Krešimir Fertalj et al. presented the optimal selection of input parameters that should be done in
Software Reliability Growth Model (SRGM). In this particular paper, Weibull model is used to
test the reliability of existing installed ERP software and for its further modification. The study
elaborates the steps that should be taken by an end user to check the reliability and to predict the
trend of the ERP product during its usage. Measurement based analysis represents a good
foundation for the future work in modelling of ERP software. [4]


F. Urem et.al. attempted to model the probability of bugs in the ERP software after completely
upgrading it. The method used is Weibull probability density function (PDF). The up gradation
of ERP software, which is a necessity, evolves the complexity of the software and hence induces
probability of increasing the bugs unless regular work is done to reduce and maintain it. [5]




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Chin-Yu Huang et al. studied the testing effort and the efficience on the modeling of software
reliability. The cost of optimal release of the software was another area studied by the author.
Testing efficience and the efforts made in testing were the presentation made by the author. [6]


Kristine B. Walhovd studied about the sample on which reliability testing for ERP measures
were performed comprised of age groups from 21 years to 92 years. The amplitude measures
were more reliable at all electrodes as compared to latency measures. The test was done by
dividing young and adult age groups into T1 and T2 with a separation of 12-14 months. [7]


Sally Wright et al. revealed an understanding of the risks of enterprise resource planning
systems (ERP) for consideration in providing information systems assurance services. Increase in
the potential for control weaknesses and resulting financial statement errors, inaccurate internal
information due to reengineering techniques and customization efforts are explored in the above
studies. [8]


Yoshinobu Tamura et al. created a fusion of neutral network and software reliability growth
model. The author has presented new approach of software reliability growth model. Numerous
cases and examples have been quoted to analyze the actual fault count. Then, for open source
software the efficiency have been considered in the mention paper. [9]


Wangbong Lee et al. They presented as an approach to software reliability assessment of OSS
adopted software system in the early stage. It shows the inadequacy of the conventional
reliability models to test the reliability of an OSS (Open source software) as OSS can be
modified but the COTS (Commercial off The-Shelf) cannot. [10]


Swapna S. Gokhale et al. A conventional approach followed in the reliability testing of software
is Black Box in which a system is considered as a whole. It is modulated on the basis of
interaction with outside world which seems to neglect the inner structure, predicting the
reliability of a software system based on its architecture, and the failure behavior of its
components, is thus essential. This paper proposes a unifying framework for state-based models
for architecture-based software reliability prediction. [11]


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Xiaolin Teng et al. The authors have presented a new methodology for predicting software
reliability in the field environment. Their work differs from some existing models that assume a
constant failure detection rate for software testing and field operation environments, as this new
methodology considers the random environmental effects on software reliability. This new
methodology provides a viable way to model the user environments, and further makes
adjustments to the reliability prediction for similar software products. Based on the generalized
software reliability model, further work may include the development of software cost models
and the optimum software release policies under random field environments. [12]


                                  Table 1.2: Benchmark Reliability Models
               Models                                   Proposed by               Year      Type
J-M-Model[19]                           Z. Jelinski Paul and B. Moranda          1972     Binomial
G-O-Model[18]                           Amrit Goel and Kazu Okumoto              1979     Poisson
Execution Time Model[20]                John Musa                                1975
Hyper Exponential Model[21]             Ohba                                     1984
Weibull Model[22]                       Weibull                                  1983     Binomial
S-Shape Model[23]                        S. Yamada, M. Ohba, and S.Osaki         1983     Gamma
Duane Model[18]                         J.T Duane                                1964
Geometric Model[24]                     Paul B. Moranda                          1979     Binomial
Logarithmic Poisson Model[25]           Musa –Okumoto                            1984     Poisson
LV Reliability Growth Model[26]         A. Ghaly, P. Chan, & B. Littlewood       1986     Gamma



1.5 Problem Genesis
There is currently a need for a creditable end-to-end software reliability model that can be
directly linked to reliability prediction from the very beginning (i.e. software design), so as to
establish a systematic SRE procedure that can be certified, generalized and refined. The cost of
correcting a software error generally increases by magnitudes for every phase of the life cycle.
Ideally most of the errors are detected by the end of the unit testing phase. Ideally, the errors
found during the integration phase are those due to interfaces that could not have been easily or
possibly found during previous phases. Ideally the number of errors detected levels off by the
acceptance test phase. If it is known what the average cost of fixing a bug is during each phase of
the life cycle, it can be estimated what the cost of repair is and also what it could be. If the
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average cost is not known, then the relative cost may be found by comparing real errors detected
over time against the ideal. Although, there has been an extensive research work being conducted
in the area of software reliability, but there is extremely less work being focused on open source
software, especially of Enterprise Resource Planning type, which the current business has high
demands. According literature survey many of the previous works on software reliability have
used secondary data source for carrying out their research. There is lot of scope for researchers to
use primary data source and conduct reliability analysis on open source Enterprise resource
planning software. Hence, this poses a high research gap in the area of open source ERP
applications in terms of software reliability.


1.6 Objectives
     To study on open source ERP software package and understand various business
        processes.
     To study different parameters that affects the reliability of an open source ERP software.
     To perform black box testing and identify bugs in open source ERP software packages.
     To improve precision of estimation of software architect reliability of ERP system of
        manufacturing.




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 2.0 THEORETICAL CONCEPTS AND FUNDAMENTALS
The Institute of Electrical and Electronic Engineers (IEEE) defines software reliability as the
probability that software will not cause a system failure for a specified time under specified
conditions. The probability is a function of the existence of faults in the software. The inputs to
the system determine whether existing faults, if any, are encountered. John Musa of AT&T Bell
Laboratories defines software reliability as the probability that a given software system operates
for some time period without software error, on the machine for which it was designed, given
that it is used within design limits. The measurement and analysis techniques include software
metrics, software reliability models, and software analyses such as fault trees and failure modes
effects and critically (FMECA). Software metrics are measures of some aspect of the software
product or process itself. Software reliability models, for the most part, model the failures
occurring because of the software. Software analysis enables development personnel to find
errors in the software while the software is still in a laboratory environment.


There are at least four major reasons why reliable software has become a very important issue in
the last decade or so.
    1. Systems are becoming software intensive. Mainly flight systems are becoming more
        software intensive than hardware intensive. Financial systems including teller, automated
        teller, and loan processing are software intensive. Defense and energy systems are
        becoming more software intensive. Everything from insurance rates to credit histories to
        hotel reservations to long-distance telephone calls is performed by software. Software
        affects our daily lives.
    2. Many software-intensive systems are safety critical. Flight systems, electronic warfare
        systems, radar, air traffic control, medical systems, energy systems, and space systems
        are all software-intensive systems that are also safety critical. Even systems that are not
        safety critical may be mission critical, meaning that success is critical to some end
        purpose (such as defeating an enemy at war), or failure is extremely costly financially.
    3. Customers are requiring more reliable software. Many government contracts are now
        requiring that an established level of software reliability be achieved. Software has also
        become part of the system reliability allocations on many government contracts.
        Commercial clients are also requiring more reliable systems, and many are attempting to

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        establish the same criteria as the government for developing of reliable software. At one
        time, software reliability was assumed to be 1 for purposes of determining system
        reliability. Those days are behind us.
    4. Software errors are not being tolerated by end users or by clients of end users. Financial
        institutions, medical institutions, the government, communication corporations, and other
        corporations are in a position of being legally liable for software that is not accurate, that
        causes potential loss of life or loss of mission, that causes inconvenience to end users,
        and that causes end users to lose profits. In addition to being liable, users and developers
        of software are also facing increasing maintenance costs.
    5. The cost of developing software is increasing. Data from a variety of sources show that
        for many systems developing the software is becoming one of the major costs of the
        system, if not the major cost. Much of the software cost can be associated with corrective
        action, particularly corrective action late in the development cycle. The cost of
        maintaining software has been shown in some studies to be as much as 40-70% of the
        total development cost. Some NASA and Air Forces have estimated it to be 50% of their
        development cost.

2.1 Software Reliability Modeling
A software reliability model specifies the general form of the dependence of the failure process
on the principal factors that affect it: fault introduction, fault removal, and the operational
environment. These models are used to predict how much more time the software needs to be
tested to achieve the desired failure intensity and to predict the expected reliability of the
delivered software. The model parameters may be determined by means of the following:
(1) Estimation: measures the reliability by applying statistical inferences to the collected failure
data. This method validates the goodness of the model by assessing its current reliability.
(2) Prediction: measures the future software reliability using the available software metrics.
The failure data that is used in the reliability models may be of two types:
(1) Failure count data which is expressed as the number of failures in each time interval.
(2) Time between failure data which is expressed as the time interval between consecutive
failures.
One type of input data can be transformed to the other to the alternate models either by using the
cumulative failure data or by using some of the existing reliability tools such as CASRE and

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SMERFS. A well defined software reliability model can determine important characteristics of
the failure process by incorporating expressions for the average number of failures experienced
at any point in time, the average number of failures in a time interval, the failure intensity at any
point in time, and the probability distribution of failure intervals. A good software reliability
model, based on sound assumptions, gives better projection of future failure behavior, computes
useful quantities, is simple, and is widely applicable.


2.2 Software Reliability Model Classification
One of the early reliability models, which was based on hardware reliability concepts, was
developed by Duane. In the seventies, many software reliability models were proposed,
developed and widely used. Since then, many different software reliability models have been
developed and numerous researchers in software reliability engineering have attempted to
categorize and classify them. Classify the reliability models in terms of five attributes:
(1)Time domain: either calendar or execution time.
(2)Category: either finite or infinite number of failures. For the finite number of failures category
models, there are a number of classes depending on the functional form of the failure intensity in
terms of time. For infinite failure category models, there are a number of families depending on
the functional form of the failure intensity in term of the expected number of failures
experienced.
(3) Type: the distribution of the number of failures experienced as a function of t.
(4) Class: functional form of the failure intensity expressed in terms of time (for finite failure
category only).
(5) Family: functional form of the failure intensity expressed in terms of the expected number of
failures experienced (for infinte failure category only).
For the sake of simplicity, first separate the finite from the infinite models. Then they incorporate
the five attributes as a guide to finding the relationship between the models; thus clarifying the
comparison between the models. The simplicity of this classification explains its popularity of
usage. Two main categories of software reliability models:
(1) software growth reliability models that estimate reliability using the error history.
(2) statistical models that estimate the reliability using the results (success or failure) of
executing test cases.

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The software growth reliability models are classified based on the nature of the failures. The time
between failures models, failure counts models, and fault seeding and input domain based
models. Categorize models into two major types:
(1) Type I : time between successive failures models, which breaks down to failure rate Type I-1
and random function Type I-2.
(2) Type II : the number of failures up to a given time.
Classify reliability models as follows:
(1) Data-domain models: A better reliability estimate can be achieved if all of the combinations
of the inputs are identified and the outcomes are well observed. To implement this theory, this
model category is decomposed into fault-seeding models and input-domain models. (2) Time-
domain models: model the failure process using the failure history to estimate the number of
faults and the required test time to uncover these faults. Homogeneous Markov, non-
homogeneous Markov, semi-Markov are models that belong to the time-domain model. Classify
software reliability according to software development life cycle phases. Their classification is
well defined and comprehensive.


                           Table 2.1. Software Reliability Growth Model Examples
 Model          Model          μ(t)                          Reference      Comments
 Name           Type
 Goel-Oku       Concave        a(l-e-bt)                     Goel,79        Also called Musa model or
 moto (G-O)                    a  0, b>O                                   exponential model
 G-OS-          S-Shaped       a( 1-(1+bt)e-bt)              Yamada,83      Modification of G-O model to
 Shaped                        a  0, b>O                                   make it S-shaped (Gamma
                                                                            function        instead         of
                                                                            exponential)
 Hossain-       Concave        a( l-e-bt)/(1+ce-bt)          Hossain,93     Solves a technical condition
 Dahiya/G-                     a  0, b>O, c>0                              with the G-O model. Becomes
 O                                                                          same as G-O as c approaches
                                                                            O.
 Gompertz       S-Shaped       a(bct)                        Kececioglu,,   Used    by Fujitsu,       Numazu
                               a  0, 0  b  1, 0<c<1
                                                             91             Works
 Pareto         Concave        a(l-(l+t/  )l-              Littlewood,    Assumes        failures       have
                                                             81             different   failure   rates   and

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                              a  0,  >0, 0    1                                    failures with highest rates
                                                                                        removed first
 Weibull        Concave       a(l-e-btC)                                    Musa,87     Same as G-O for c=1
                              a  0, b>0,c>0
 Yamada         Concave       a(1-e-r    (1-e-    t))                     Yamada,86   Attempts to account for testing
 Exponential                  a  0, r  >0,  >0                                       effort

 Yamada         S-Shaped                           (  t 2 / 2 )           Yamada,86   Attempts to account for testing
                               a(1  e  r (1e                    )
                                                                        )
 Raleigh                                                                                effort
 Log            Infinite      (l/c)ln(c  t+l)                              Musa,87     Failure rate decreases but does
 Poisson        Failure       c>O,  >O                                                 not approach 0



2.3 Theoretical Comparison of Techniques
This section compares the three parameter estimation techniques from a theoretical perspective.
We focus on their ease of use, confidence interval shape, and parameter scalability. Since
optimization packages are readily available, maximum likelihood, classical least squares, and
alternative least squares are all straightforward to solve. However, maximum likelihood only
applies to the G-O model, and a new maximum likelihood equation must be derived for each
software reliability growth model. These equations can be difficult to derive, especially for the
more complex models. Classical least squares applies to the exponential family of models that
includes the G-O model. It is fairly easy to modify this equation for similar models. Alternative
least squares are the easiest to use since it applies to any software reliability growth model, so the
alternative least squares method is the easiest to apply. Confidence intervals for all of the
estimation techniques are based on assuming that estimation errors are normally distributed. For
the maximum likelihood technique, this assumption is good for large sample sizes because of the
asymptotically normal properties of this estimator. However, it is not as good for the smaller
samples that we typically have. Nevertheless, the maximum likelihood technique provides the
best confidence intervals because it requires less normality assumptions and because it provides
asymmetric confidence intervals for the total defect parameter. The lower confidence limit is
larger than the number of experienced defects, and the upper confidence limit is farther from the
point estimate than the lower confidence limit to represent the possibility that there could be
many defects that have gone undetected by testing. Conversely, for the least squares techniques,
the lower confidence limit can be less than the number of experienced defects (which is
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obviously impossible), and the confidence interval is symmetric. Also, additional assumptions
pertaining to the normality of the parameters are necessary to derive confidence intervals for the
least squares techniques. The transformation technique consists of multiplying the test time by an
arbitrary (but convenient) constant and multiplying the number of defects observed by a different
arbitrary constant. For this technique to work, the predicted number of total defects must be
unaffected by the test time scaling and must scale the same amount as the defect data. For
example, we may experience 50 total defects during test and want to scale that to 100 for
confidentiality or ease of reporting. To do that transformation, the number of defects reported
each week must be multiplied by 2. If 75 total defects were predicted by a model based on the un
scaled data, then the total defects predicted from the scaled data should be 150.
Reliability growth models are categorized as hardware models and software models.
   Hardware models – using for electronics systems, functions blocks, electronics components
    without connections to software. Basic terms are a reliability operating state and a failure.
    There are two basic types of failures during the development phase – random failures, early
    failures.
   Software models –using for software.
   Probabilistic reliability growth models – because of no unknown parameters associated with
    these models, the data obtained during the program cannot be incorporated.
   Statistical reliability growth models – unknown parameters are associated with these models.
    In addition, these parameters are estimated throughout the development of the product in
    question.
   Time independent reliability growth models – number of failures or repairs in definite time
    interval are not depended on time.
   Time dependent reliability growth models – a reliability growth model is function of time.
   Continuous reliability growth models – these are time models.
   Discrete reliability growth models – these are useful for unrecoverable objects, there are two
    discrete states – a reliability operating state or a failure.
   Classically reliability growth models – mathematical equipment is theory of probability,
    Duane reliability growth model and its modifications – these are continuous, time dependent
    and statistical models,



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   Stochastic reliability growth models – a reliability growth is non-stationary stochastic
    process – non-stationary stochastic Poisson process.
   Bayes and quasi-Bayes reliability growth models
   Unconventional reliability growth models – there are all reliability growth models, for which
    is no existing possibility to arrange in classification categories.


2.4. Exponential Models
All software reliability models of the exponential class have a common set of assumptions. In
addition to these common assumptions, each model has its own unique set of assumptions. The
standard assumptions are (Lyu 1995): The software is operated in a similar manner as that for
which reliability predictions are to be made. This assumption is to ensure that the data collected
in that particular environment is applicable to the environment in which the reliability
projections are to be made. Every fault has the same chance of being encountered and is of the
same severity as any other faults. This assumption is to ensure that the various failures all have
the same distributional properties. One severity class might have different failure rates which
may require separate reliability analysis. The failures are independent. A failure occurs when the
faults are encountered, so having independent failures simplifies the maximum likelihood
estimates. Exponential class models have two major types: binomial-type and Poisson-type. In
addition to the common assumptions for the exponential class models, the binomial-type models
assume that the failures are removed from the software as soon as they occur and introduce no
more faults during the fix. The Poisson-type models assume that the faults remaining in the
software is a Poisson random variable. The principal difference between the binomial and
Poisson type models is how the remaining number of faults are treated. Considering equations
(3) and (4), binomial- type models treat the number of remaining faults as a fixed number. While
in the Poisson-type models, the number of remaining faults is treated as random variable (Musa,
Iannino, Okumoto 1987). Jelinski-Moranda, Musa and Schneidewind models are some the
different reliability models. Table 1 summaries the reliability functions for these models.




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         Table 2.2: Summary of reliability functions for Jelinski-Moranda, Musa and schn. Models
        Reliability          Jelinski-Moranda               Musa’a Model               Schneidewind’s
        Functions                  Model                                                     Model
           F(t)                    1  e t
                                                             1 e
                                                                           
                                                                                1t             
                                                                                       /   1  e  t 
             f(t)                     e t                     1e   
                                                                           t
                                                                                             e t
             z(t)                                                  1                       e  t
                                                                                     1   /    e  t 
                                                                                                  1
             (t )                
                                N 1  e t                  0 1  e   1t        /   1  e  t 
             (t )                N e t                      0 1e   1t              e  t




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                                  3.0 METHODOLOGY
The following methodology is used to identify error data in the Ofbiz ERP software package.
The data collection process is the most critical prerequisites to measuring software reliability.
The effectiveness of any reliability measurement will be directly related to the effectiveness of
collecting the data necessary for measurement.
       Study of Ofbiz ERP software package.
       Develop operational profile.
       Testing
       Data analysis

3.1 Study of Ofbiz ERP software package.
The first step in methodology is to thoroughly understand various business processes existing in
Ofbiz ERP software package. It is very much essential to understand various business processes
before the testing of the software. They are various modules available in Ofbiz software such as
catalog manager, bill of materials, purchase module, sales module etc. Key is to understand the
integration of all modules.

3.2 Develop operational profile
An Operational Profile is simply the set of Operations and their probabilities of occurrence. Most
frequently used modules are given more emphasis. More Test cases have to be generated for
those modules which are frequently used. In this work the modules which are identified are
catalog manager, bill of materials, purchase module and sales module.

3.3 Testing
Software testing is an important technique for assessing the quality of software product. Two
basic software testing methods are Black Box testing and White Box testing. In this work the
software is tested using black box testing. Black Box testing includes performance testing and
functionality testing.


3.3.1 Performance testing on different operating systems
The performance test is carried to identify how this Ofbiz ERP software package operates on
different operating systems such as Windows XP, Windows Vista, Windows 7, Ubuntu.


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                                   Table 3.1: Evaluations of Ofbiz in OS
Name of Evaluation            Evaluation of OFBiz On OS
Object of Evaluation          OFBiz running on OS on 3GB RAM


Test Case Used                Execution of OFBiz in 64 bit OS
                                           a. Windows XP
                                           b. Windows Vista
                                           c. Windows 7 Home Basic
                                           d. Ubuntu



Actual Output                 In Execution of OFBiz in bit 64Windows professional OS
                                           a. The performance is better compared to previous
                                               version. But optimum performance varies if RAM is
                                               enhanced.
                                           b. Performance       is     much   better   w.r.t   installation,
                                               navigation, configuration
                                           c. Same as previous versions of Windows.
                                           d. Performance is much better compared to windows



Remarks                       Successful performance in windows XP , Vista, 7 and Ubuntu




3.3.2 Performance testing on different web browsers.
After testing the performance of software on different operating systems next step is to test Ofbiz
software performance on different web browsers. Mozilla Firefox, Internet Explorer and Google
Chrome are identified browsers for testing.




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                                 Table 3.2: Ofbiz performance for Browser
 Name of        Testing                OFBiz Testing                      OFBiz Testing                    OFBiz
 Evaluation performance on Mozilla performance on Internet performance on Google
                Firefox version 13.0            Explorer 9                         Chrome
 Object of      Firefox performance with IE-9.0 performance with Google                                   Chrome
 Evaluation     OFBiz                           OFBiz                              performance with OFBiz
 Test Case          1. Navigation          of         5. Navigation           of      9. Navigation            of
 Used                   pages                            pages                             pages
                    2. Cookies                        6. Cookies Clearance            10. Cookies Clearance
                        Clearance                     7. History Scrutiny             11. History Scrutiny
                    3. History Scrutiny               8. Page Visualization           12. Page Visualization
                    4. Page
                        Visualization
 Actual             1. Navigation          of         5. Navigation           of      9. Navigation            of
 Output                 Pages    are    very             Pages      are     very           pages     are    quite
                        smooth                           smooth                            faster
                    2. Proper          cookie         6. Cookies     are     not      10. In Google chrome,
                        management                       managed properly                  incognito window
                    3. Proper history and             7. No proper history                 enables the better
                        cache                            and               cache           cookies clearance.
                        management                       management                   11. Better           history
                    4. The      pages     are         8. The      pages      are           management         due
                        visually better to               visually     not     so           to         incognito
                        experience                       better               to           window.
                                                         experience as user           12. The               pages
                                                                                           visualization        is
                                                                                           better compare to
                                                                                           internet explorer 9
 Remarks        Mozilla Firefox seems to IE-9.0 seems not to be best Google chrome is also
                be best compatible with compatible with OFBiz                      found        to   be      best
                OFBiz                                                              compatible browser after
                                                                                   Firefox Mozilla.

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3.4 Data collection
Software testing is an important technique for assessing the quality of software product. Two
basic software testing are Black Box testing and White Box testing. Black Box testing includes
performance testing and functionality testing. In performance testing, data is collected on basis
of working of modules in different browsers in single operating system and also in different
operating system.
From the study found that software failure follows exponential distribution, for example suppose
we test to failure a large number of software operation, for each small unit of time, if we
calculate the failure rate and plot the same against time t, the resulting graph is the failure rate
curve.


                                                                       The value of reliability R(t) is 1 at
                                                                       t = 0 and it decreases continuously
                                                                       with time when (t) is large, all the
                                                                       software will have failed giving
                                                                       R(t) = 0 at t    ∞
                                                                       It can be proved that in case of
                                                                       constant failure rate,
                                                                       R=
                                                                       λ = constant failure rate
                                                                       T = mission time.
                                                                       We      conducted    experiment   on
                                                                       oFbiz     open      source   software
creating bill of material ,purchase order, sales order and purchase order through MRP run are
some of certain operations which has been frequently used in many organizations. By creating
bill of material and time is noted down, these has been repeated for ten times and tabulated,
average time of ten reading have calculated and standard time has been found, standard time is
sum of average time and 10% of average time. Then based on standard time failure rate (λ) is
calculated, failure rate is number of time creating a bill of material exceeded standard time, same



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procedure is been carried to other ERP modules and then the reliability of software in particular
web browser for particular operating system is calculated.
Standard time = Basic average time + (10%* Basic average time)
Failure rate (λ) = No. Of operations exceeds standard time/total no. of trails


3.4.1 Performance testing of different modules of Ofbiz on different Web
browsers.
The important modules which are identified in operational profile stage are being tested on
different web browsers. Time taken to create bill of materials, purchase order, sales order and
purchase order through MRP are noted down for different web browsers.
Readings of 10 trails for different browsers with different operating system for processing time
of different operations shown in below tables :-


                                 Table 3.3: Windows XP –Internet Explorer
                                                                             Purchase order
                                Bill of       Purchase            Sales         through
                    Sl.no      material          order            order          MRP
                      1           9.3             4.2               4.2          5.6
                      2            9              4.1                4           5.4
                      3           8.9             3.8               3.8          5.1
                      4           8.4             3.7               3.5          4.9
                      5           8.2             3.5               3.4          4.6
                      6           8.1             3.4               3.1          4.4
                      7           7.9             3.2               2.8          4.2
                      8           7.8             2.9               2.7           4
                      9           7.7             2.6               2.3          3.9
                     10           7.7             2.6               2.2          3.9
                    sum           83               34               32           46
                    Avg           8.3             3.4               3.2          4.6
                                          Note all readings are in minutes




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                                  Table 3.4: Windows XP –Mozilla Firefox


                                                                                 Purchase order
                                  Bill of           Purchase           Sales        through
                    Sl.no        material             order           order          MRP
                      1                9.3              4.3             4.3           5.6
                      2                 9               4.2             4.1           5.4
                      3                8.9              3.9             3.9           5.1
                      4                8.4              3.8             3.6           4.9
                      5                8.2              3.6             3.5           4.6
                      6                8.1              3.5             3.2           4.4
                      7                7.9              3.3             2.9           4.2
                      8                7.8                 3            2.8            4
                      9                7.7              2.7             2.4           3.9
                     10                7.7              2.7             2.3           3.9
                     sum               83               35               33           46
                     Avg               8.3              3.5             3.3           4.6
                                             Note all readings are in minutes.



                                  Table 3.5: Windows XP –Google Chrome
                                                                                 Purchase order
                                Bill of           Purchase            Sales         through
                    Sl.no      material             order            order           MRP
                      1           9.2                 4.3              4.3            5.5
                      2           8.9                 4.2              4.1            5.3
                      3           8.8                 3.9              3.9             5
                      4           8.3                 3.8              3.6            4.8
                      5           8.1                 3.6              3.5            4.5
                      6            8                  3.5              3.2            4.3
                      7           7.8                 3.3              2.9            4.1
                      8           7.7                  3               2.8            3.9
                      9           7.6                 2.7              2.4            3.8
                     10           7.6                 2.7              2.3            3.8
                    sum           82                  35                33            45
                    Avg           8.2                 3.5              3.3            4.5


                                             Note all readings are in minutes.


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                               Table 3.6: Windows Vista – Internet Explorer
                                                                                 Purchase order
                                 Bill of            Purchase            Sales       through
                  Sl.no         material              order             order        MRP
                    1             9.3                    4               4.5       5.7
                    2              9                    3.9              4.3       5.6
                    3             8.7                   3.6              4.1       5.4
                    4             8.6                   3.5              3.8       5.1
                    5             8.5                   3.3              3.7       4.8
                    6             8.3                   3.2              3.4       4.6
                    7             8.2                    3               3.1       4.4
                    8             8.2                   2.7                3       4.2
                    9             8.1                   2.4              2.6       4.1
                   10             8.1                   2.4              2.5       4.1
                  sum              85                   32                35       48
                  Avg             8.5                   3.2              3.5       4.8
                                           Note all readings are in minutes.




                                 Table3.7: Windows Vista –Mozilla Firefox
                                                                                   Purchase
                               Bill of               Purchase            Sales   order through
                  Sl.no       material                 order            order        MRP
                   1             9.6                     4.2              4.6            6
                   2             9.4                     4.1              4.4           5.9
                   3             9.2                     3.8              4.2           5.7
                   4             8.9                     3.7              3.9           5.4
                   5             8.6                     3.5              3.8           5.1
                   6             8.5                     3.4              3.5           4.9
                   7             8.3                     3.2              3.2           4.7
                   8             8.2                     2.9              3.1           4.5
                   9             8.1                     2.6              2.7           4.4
                   10            8.2                     2.6              2.6           4.4
                  sum            87                      34               36             51
                  Avg            8.7                     3.4              3.6           5.1
                                           Note all readings are in minutes.


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                                 Table 3.8: Windows Vista –Google Chrome
                                                                                         Purchase
                                                       Purchase                Sales   order through
                   Sl.no       Bill of material           order                order         MRP
                     1               9.6                   4.3                  4.3     5.9
                     2               9.3                   4.2                  4.1     5.8
                     3               9.2                   3.9                  3.9     5.6
                     4               8.7                   3.8                  3.6     5.2
                     5               8.5                   3.6                  3.5     5.1
                     6               8.4                   3.5                  3.2     4.8
                     7               8.2                   3.3                  2.9     4.7
                     8               8.1                    3                   2.8     4.3
                     9                8                    2.7                  2.4     4.3
                    10                8                    2.7                  2.3     4.3
                   sum               86                     35                   33      50
                   Avg               8.6                   3.5                  3.3      5

                                           Note all readings are in minutes.




                                  Table 3.9: Windows 7 – Google Chrome.
                                                                                       Purchase
                                                                                         order
                                                      Purchase                 Sales    through
                  Sl.no       Bill of material           order             order         MRP
                    1               9.5                   3.8                   4.2       5.1
                    2               9.2                   3.7                   4         4.8
                    3               9.1                   3.4                   3.8       4.7
                    4               8.6                   3.3                   3.5       4.6
                    5               8.4                   3.1                   3.4       4.4
                    6               8.3                     3                   3.1       4.2
                    7               8.1                   2.8                   2.8       3.7
                    8                8                    2.5                   2.7       3.6
                    9               7.9                   2.2                   2.3       3.5
                   10               7.9                   2.2                   2.2       3.4
                  sum               85                     30                   32           42
                  Avg               8.5                     3                   3.2       4.2
                                           Note all readings are in minutes.


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                                  Table 3.10: Windows 7 – Mozilla Firefox
                                                                                         Purchase
                                                                                          order
                                                       Purchase                 Sales    through
                  Sl.no       Bill of material            order             order         MRP
                    1               8.9                     4.3                  4.7       5.4
                    2               8.7                     4.2                  4.5       5.1
                    3               8.6                     3.9                  4.3        5
                    4               8.1                     3.8                  4         4.9
                    5               7.9                     3.6                  3.9       4.7
                    6               7.8                     3.5                  3.6       4.5
                    7               7.6                     3.3                  3.3        4
                    8               7.5                      3                   3.2       3.9
                    9               7.5                     2.7                  2.8       3.8
                   10               7.4                     2.7                  2.7       3.7
                  sum               80                      35                   37        45
                  Avg                8                      3.5                  3.7       4.5
                                         Note all readings are in minutes.


                                 Table 3.11: Windows 7 – Internet Explorer
                                                                                          Purchase
                                                                                           order
                                                          Purchase               Sales    through
                    Sl.no      Bill of material             order                order     MRP
                        1             8.5                     3.3                 3.6       4.3
                        2             8.3                     3.2                 3.4       3.8
                        3             8.3                     2.9                 3.2       3.7
                        4             7.7                     2.8                 2.9       3.6
                        5             7.5                     2.6                 2.8       3.4
                        6             7.4                     2.5                 2.5       3.3
                        7             7.2                     2.3                 2.2       2.7
                        8             7.1                        2                2.1       2.5
                        9             7.1                     1.7                 1.7       2.4
                     10               6.9                     1.7                 1.6       2.3
                    sum               76                      25                  26         32
                    Avg               7.6                     2.5                 2.6       3.2
                                            Note all readings are in minutes.



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                                   Table 3.12: Ubuntu– Internet Explorer Firefox
                                       Bill of
                        Sl.no         material             Sales order              Purchase Order
                          1              8.2                     3.2                3.2
                          2              8.1                      3                 3.1
                          3               8                      2.9                2.9
                          4              7.6                     2.7                2.5
                          5              7.4                     2.6                2.5
                          6              7.2                     2.5                2.3
                          7              7.1                     2.1                2.2
                          8               7                      1.9                1.9
                          9              6.8                     1.7                1.7
                         10              6.6                     1.4                1.7
                        Sum              74                      24                  24
                        Avg              7.4                     2.4                2.4
                                                Note all readings are in minutes.




                                    Table 3.13:Ubutu– Internet Explorer Firefox
                                          Bill of                  Sales            Purchase
                           Sl.no         material                  order             Order
                               1               8.4                     4                  3.9
                               2               8.3                     3.8                3.8
                               3               8.2                     3.7                3.6
                               4               7.8                     3.5                3.2
                               5               7.6                     3.4                3.2
                               6               7.4                     3.3                3
                               7               7.3                     2.9                2.9
                               8               7.2                     2.7                2.6
                               9               7                       2.5                2.4
                              10               6.8                     2.2                2.4
                              Sum              76                      32                 31
                              Avg              7.6                     3.2                3.1
                                                Note all readings are in minutes.




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                                 Table 3.14 : Ubuntu– Internet Explorer Firefox
                                         Bill of            Sales
                           Sl.no        material           order          Purchase Order
                             1            8.3                3.7               3.7
                             2            8.2                3.5               3.6
                             3            8.1                3.4               3.4
                             4            7.7                3.2               3
                             5            7.5                3.1               3
                             6            7.3                 3                2.8
                             7            7.2                2.6               2.7
                             8            7.1                2.4               2.4
                             9            6.9                2.2               2.2
                            10            6.7                1.9               2.2
                           Sum             75                29                29
                           Avg            7.5                2.9               2.9
                                           Note all readings are in minutes.




3.4.2 Functionality Testing

Functional testing is a type of black box testing that bases its test cases on the specifications of
the software component under test. Functions are tested by feeding them input and examining the
output, and internal program structure is rarely considered.

Functional testing differs from system testing in that functional testing " a program by checking
it against design document or specification", while system testing " a program by checking it
against the published user or system requirements"

Functional testing typically involves five steps:-

    1. The identification of functions that the software is expected to perform
    2. The creation of input data based on the function's specifications
    3. The determination of output based on the function's specifications
    4. The execution of the test case
    5. The comparison of actual and expected outputs

Department of industrial engineering and management                                          Page 35
R.V. College of Engineering


3.4.2.1 TEST CASES

                                            Table 3.15: Test cases


 Step.      Step Description        Test Data         Actions        Expected   Actual     Status
 no                                                                  Results    Result
 T01        Examine entering Business                 Click     on Loged-in     Loged-in   Pass
            username           & 12345                Login
            password
 T02        Examine entering Business                 Click     on Pop-up box Pop-up box   Pass
            correct username 12366                    Login
            &             wrong
            password
 T03        Examine entering Business                 Click     on Pop-up box Pop-up box   Pass
            wrong      username 12345                 Login
            &             correct
            password
 T04        Examine entering Business                 Click     on Pop-up box Pop-up box   Pass
            wrong      username 12356                 Login
            &             wrong
            password
 T05        Ensure after log-in Business              Click     on Open         Open       Pass
            should      go     to 12345               Login          homepage   homepage
            homepage
 T06        Check BOM on Create all Click                       on BOM pdf      BOM pdf    Pass
            screen                  product           BOM
                                    give price, simulation
                                    supplier
                                    and
                                    facilities
 T07        Check BOM on Create                       Click     on Should not Should not Pass
            screen                  product           BOM            show BOM show BOM

Department of industrial engineering and management                                          Page 36
R.V. College of Engineering


                                    Without           simulation
                                    giving
                                    facilities
 T08        Examine         sales Input               Click      on Should      Should give Pass
            enquiry                 enquiry           supplier      give        supplier
                                    number                          supplier    sales
                                                                    sales
 T09        Examine product Input                     Click      on Display     Display     Pass
            detail                  product           product       product     product
                                    name/ID           detail        detail      detail
 T10        Examine entering Id: cap                  Click      on Show error Show error Pass
            same product id & name: cap               create        that        that
            product name                              product       duplicate   duplicate
                                                                    key arise   key arise
 T11        Examine entering Id: cap                  Click      on Product     Product     Pass
            different    product name: caps create                  created     created
            id & product name                         product
 T12        Edit product detail     Product           Click      on Go to edit Go to edit Pass
                                    detail            edit          screen      screen
                                                      product
 T13        Examine product Engine                    Click      on Pop-up box Pop-up box   Pass
            ID                      lathe             create
                                                      product
 T14        Examine product Engine                    Click      on Product     Product     Pass
            ID without space        _lathe            create        created     created
                                                      product
 T15        Examine product Without                   Click      on Pop-up box Pop-up box   Pass
            without      internal internal            create
            name                    name              product
 T16        Examine product With                      Click      on Pop-up box Pop-up box   Pass
            with internal name internal               create


Department of industrial engineering and management                                           Page 37
R.V. College of Engineering


                                    name              product
 T17        Creating suppliers Product                Click     on Pop-u box     Pop-u box        Pass
            for subassemblies id:__                   create
            without product id Product
            & name                  name:__
 T18        Creating suppliers Product                Click     on Pop-u box     Pop-u box        Pass
            for subassemblies id:chuck9               create
            with product id & Product
            name                    name:
                                    chuck
 T19        Changing                Indian            Click     on Not           Not              Pass
            currency in price rupee             to edit            applicable    applicable
            module                  USD               product
                                                      price
                                                      update
 T20        Creating                Select            Click     on Should        Should           Pass
            manufacturing           facility has BOM               show       all show      all
            BOM                     manufactu         simulation   elements      element
                                    ring BOM
 T21        Creating                Select            Click     on Should        Should           Pass
            engineering BOM         facility has BOM               show only show          only
                                    engineerin        simulation   parent        parent
                                    g BOM                          element       element
 T22        Examine without Create                    Click     on Pop-up box Pop-up box          pass
            selecting shipping sales            or finalize
            address                 purchase          order
                                    order
                                    without
                                    selecting
                                    address
 T23        Examine selecting Create                  Click     on Sales order Sales order Pass


Department of industrial engineering and management                                                 Page 38
R.V. College of Engineering


               shipping address     sales       or finalize       or purchase or purchase
                                    purchase          order       order      is order      is
                                    order with                    created       created
                                    selecting
                                    address
    T24        Examine payment Create                 Select      Should        Should          Pass
               mode                 sales       or shipping       show      the show      the
                                    purchase          address     payment       payment
                                    order with                    mode          mode
                                    selecting
                                    address
    T25        Examine payment Create                 without     Should not Should not Pass
               mode                 sales       or Selecting      show      the show      the
                                    purchase          shipping    payment       payment
                                    order             address     mode          mode
                                    without
                                    selecting
                                    address


3.4.2.2 Ofbiz Defect Tracker
1. Problem Description
While creating sales order through order entry, how shall we ship it? Mode for USPS standard
shown calculated offline. This is to be selected but by selecting will not allow us to carry out the
sales order.
          Bug severity:- Minor
          Found By :- Managing Maniac
          Date :- 2-4-2012

Steps to Reproduce
•    Go to application-select order manager

•    Select order entry-give product ID and click on add to order


Department of industrial engineering and management                                               Page 39
R.V. College of Engineering


•   Finalize the order-select the shipping address-shipping mode with USPS standard-payment
mode

•   Click on continue to finalize order and it should show the order conformation page.




                                        Fig.3.1: Quick finalize order


Work Around
Sales order is created using order entry in order manager, by selecting any other shipping mode
and then click on continue to final order after creating order we can change the shipping mode to
USPS standard by updating the shipping information.


2. Problem description
When a Purchase Order is received manually using the ‘receive functionality’ of Facility
Manager module, the invoice entry in accounting is not automatically raised.
        Bug severity:- Minor

        Found by :- Managing Maniac

        Date:- 6-4-2012


Department of industrial engineering and management                                        Page 40
R.V. College of Engineering




                            Fig. 3.2: Shows how to receive quick purchase order


Steps to reproduce
•       Create a purchase order using the order entry screen of Order Manager
•       Once the order is created, approve the purchase order
•       In the Facility Manager, select the facility as Kamakshi Palya Plant and receive the
inventory using ‘Receive Inventory’ option
•       Once receive inventory is successful, check the status of the PO in Order Manager. It will
be shown as ‘Complete’
•       Check for a Purchase Order Invoice entry in accounting module using the functionality
‘Find Purchase Invoice’. There should be a PO invoice corresponding to the amount of the PO
fulfilled.




Department of industrial engineering and management                                            Page 41
R.V. College of Engineering




                                          Fig.3.3: Finding Invoice


Work Around – When a purchase order is created using the order entry functionality of Order
manager, there is an option to quick receive purchase order. When a PO is received using this
option the purchase order invoice entry is made automatically. Shown below is the screenshot of
Quick receive PO functionality.
3. Problem description
When a Purchase Order is raised and approved, in the Quick Receive option, newly created
facility is not listed.
        Bug Severity – Minor, data related issue
        Found by – Managing Maniac
        Date – 10-4-2012

Steps to reproduce –
•       Create a new facility ‘Kamakshi palya Plant’ of facility type ‘Plant’

•       During the creation of the PO, in the order entry ship to settings, though the newly
created plant is listed, there is no option to select it.
Department of industrial engineering and management                                       Page 42
R.V. College of Engineering




                                            Fig.3.4: Order Entry
Work Around-In the Facility Manager, select the facility as Kamakshi Palya Plant and create a
contact mechanism of type ‘Postal Address’ and save the details




                                         Fig.3.5: Facility Manager




Department of industrial engineering and management                                   Page 43
R.V. College of Engineering


Try to create the purchase order again, as you can see in the screenshot below the newly created
plant is visible as an option to be selected for shipment




                                     Fig.3. 6: Shipping addresses update


4. Problem description – When creating a Purchase Order, in the ‘Add Item’ screen the top
menu bar is not clearly visible as shown in the screenshot below
        Bug Severity – Low, Cosmetic
        Found by – Managing Maniac
        Date – 21-4-2012



Steps to reproduce
        •   Create a purchase order using the order entry screen

        •   Select the supplier and click continue


Department of industrial engineering and management                                      Page 44
R.V. College of Engineering


        •   Enter the order name and order number and click continue

        •   In the add items screen displayed below the text is not clearly visible in top menu




                                            Fig.3.7: Screen Visuality




    Files Modified:
    ofbiz-trunkapplicationsorderwebappordermgrentryOrderEntryTabBar.ftl
    Verification: After fixing the defect, it can be seen that the options in top menu bar are
    clearly visible.




Department of industrial engineering and management                                          Page 45
R.V. College of Engineering




                                        Fig.3. 8: Screen after solving




3.5 Data Analysis

Analysis of data is a process of inspecting, cleaning, transforming, and modelling data with the
goal of highlighting useful information, suggesting conclusions, and supporting decision making.
Data analysis has multiple facets and approaches, encompassing diverse techniques under a
variety of names, in different business, science, and social science domains.

Data mining is a particular data analysis technique that focuses on modelling and knowledge
discovery for predictive rather than purely descriptive purposes. Business intelligence covers
data analysis that relies heavily on aggregation, focusing on business information. In statistical
applications, some people divide data analysis into descriptive statistics, exploratory data
analysis (EDA), and confirmatory data analysis (CDA). EDA focuses on discovering new
features in the data and CDA on confirming or falsifying existing hypotheses. Predictive

Department of industrial engineering and management                                        Page 46
R.V. College of Engineering


analytics focuses on application of statistical or structural models for predictive forecasting or
classification, while text analytics applies statistical, linguistic, and structural techniques to
extract and classify information from textual sources, a species of unstructured data. All are
varieties of data analysis.

Data integration is a precursor to data analysis, and data analysis is closely linked to data
visualization and data dissemination. The term data analysis is sometimes used as a synonym for
data modelling

Analysis is done by above collected data as follow:-




For Operating System : Windows XP




                                 Fig.3.9: Comparison graph in windows XP




Department of industrial engineering and management                                        Page 47
R.V. College of Engineering




                                  Fig.3.10: Avg. time graph in windows XP




Basic Avg. Time =No. of trials/N
Basic Avg. Time = (9.3+9+8.9+8.4+8.2+8.1+7.9+7.8+7.7+7.7)/10
Basic Avg. Time = 8.3 minutes
Standard Time = Basic Avg. Time * 10% allowance of basic Avg. Time
Standard Time = 8.3 * (8.3*0.1)
Standard Time = 9.13 minutes
Reliability(R) =
R= e -0.4*1 = 0.6703
Avg.reliability = (R1 + R2+ R3)/n
Avg.reliability = 0.7173
Avg.reliability ==71.73%




Department of industrial engineering and management                         Page 48
Reliability study and analysis on open source enterprise resource planning software package
Reliability study and analysis on open source enterprise resource planning software package
Reliability study and analysis on open source enterprise resource planning software package
Reliability study and analysis on open source enterprise resource planning software package
Reliability study and analysis on open source enterprise resource planning software package
Reliability study and analysis on open source enterprise resource planning software package
Reliability study and analysis on open source enterprise resource planning software package
Reliability study and analysis on open source enterprise resource planning software package
Reliability study and analysis on open source enterprise resource planning software package
Reliability study and analysis on open source enterprise resource planning software package
Reliability study and analysis on open source enterprise resource planning software package
Reliability study and analysis on open source enterprise resource planning software package

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Reliability study and analysis on open source enterprise resource planning software package

  • 1. R.V. College of Engineering R.V. COLLEGE OF ENGINEERING, BANGALORE-560059 (Autonomous Institution Affiliated to VTU, Belgaum) Reliability Study and Analysis on Open Source Enterprise Resource Planning Software Package MINI PROJECT REPORT Submitted by Mayank Baheti 1RV09IM024 Deepak Rathod 1RV09IM009 Suraj Soni 1RV09IM044 Tanay Agrawal 1RV09IM046 Under the Guidance of Mr. Vikram Bahadurdesai Asst. Professor, IEM Department, R.V. College of Engineering, Bangalore 560059. In Partial fulfilment of the academic requirements of VI Semester B.E Programme in Industrial Engineering and Management 2011-12 Department of industrial engineering and management Page 1
  • 2. R.V. College of Engineering Sl.No Topic Page. No Abstract i. 1. Introduction 7 1.1. Overview to Software Reliability 7 1.2 Overview of Open Source ERP Software 7 1.3. Principles of open source architecture 8 Introduction to open source architecture 8 1.3.1. Overview Apache open for business (Ofbiz) 9 1.3.2. 1.3.3. Features of Apache open for business(Ofbiz) 10 1.3.4. Different Open Source ERP Software’s 10 1.4. Literature Review 13 1.5. Problem Genesis 15 1.6. Objectives 16 2. Theoretical Concepts and fundamentals 17 2.1. Software reliability modeling 18 Department of industrial engineering and management Page 2
  • 3. R.V. College of Engineering 2.2. Software reliability modeling classification 19 2.3. Theoretical Comparison of Techniques 21 2.4. Exponential Model 23 3. Methodology 25 3.1. Study of Ofbiz ERP software package 25 3.2. Develop Operational Profile 25 3.3. Testing 25 Performance testing on different operating systems 25 3.3.1. Performance testing on different web browsers 26 3.3.2. 3.4. Data Collection 28 3.4.1. Performance testing of different modules of Ofbiz on different Web browsers. 29 3.4.2. Functionality Testing 35 3.4.3. Test cases 36 3.4.4. Ofbiz Defect Tracker 39 Department of industrial engineering and management Page 3
  • 4. R.V. College of Engineering 3.5. Data analysis 46 4. Conclusions 56 5. Future Scope 57 References Department of industrial engineering and management Page 4
  • 5. R.V. College of Engineering Abstract Software reliability turns out to be most important part of business today, which plays an important role in assuring the quality of software systems. Software reliability is built in a system by performing exhaustive testing. Testing is an important phase in software development lifecycle and is generally considered to be costly and tedious. But it is very much essential to ensure the quality of the product. The ideal goal of most of the companies is to develop robust software by testing it cost-effectively. Robustness should be inherently developed along with product development rather than incorporating it after the implementation phase. The idea is to test early and to test often. Software reliability is the probability of failure free software operation for a specified period of time in a specified environment. The high complexity of the software is the major contributing factor of software reliability problems. Software reliability is also important factor affecting system reliability. Software reliability is an important attribute of software quality, together with functionality, usability, performance, serviceability, capability, install ability, maintainability and documentation. Software reliability engineering (SRE) is an emerging discipline, SRE is the application of statistical techniques to specify, predict, estimate and assess the reliability of software based systems. Testing open source apache Ofbiz ERP software gives as opportunity to have inferences such as reliability, mean time to failure (MTTF) etc, for software system and gain its credibility in this field. Software Testing is process of executing a program with the intent of finding error. A good test case is one that uncovers an as yet undiscovered error. Testing should systematically uncover different classes of errors in a minimum amount of time and with a minimum amount of effort. Generating test sequences from usage probability distributions, assessing statistical inferences based on analytical results associated with test process and also derive Department of industrial engineering and management Page 5
  • 6. R.V. College of Engineering stopping criterion of test process. But the problem associated in this process is to test the software behavior in different types of environment and measuring the reliability. The purpose of testing can be quality assurance, verification and validation, or reliability estimation. Software testing is a trade-off between budget, time and quality. To predict reliability of software failure data need to be collected during testing phase of the software developmental life cycle. So software testing plays a crucial role in estimating the reliability of software system. In this work, Ofbiz ERP software package is tested for its performance on different operating systems and on different web browsers. Finally functionality test has been conducted by generating test cases. This result in identifying different bugs, using work around methods bugs has been fixed. Our experimental efforts lead us to a more practical and effective approach for software reliability. Department of industrial engineering and management Page 6
  • 7. R.V. College of Engineering 1.0 INTRODUCTION 1.1 Overview of Software Reliability Software reliability is one of the major software quality attributes, which quantitatively expresses the continuity of correct service delivery. Reliability models are typically measurement based models, and mostly employed in isolation at the later stage of the software development process. In current practice, early software reliability prediction models are often insufficiently formal to be analyzable and not usually connected to the target system. Additionally, despite the vast work that has been done in software reliability, much work is still needed, especially in the component-based development arena regarding availability of software component reliability information following a clear failure classification scheme. Aiming at addressing these problems, this work contributes a novel reliability prediction technique that leverages reliability analysis in early stages of software development by taking into account, the component structure exhibited in the scenarios elicited in the requirements phase and the concurrent nature of component-based systems. Following that contribution, this thesis proposes a means to accomplish reliability design and analysis for model driven engineering following the Model Driven Architecture standards. By doing that, this research work contributes to the task of systematically integrating reliability modeling from the early to the late stages of software engineering and thus semantically integrating analysis, design and deployment models for reliability into one environment. Open Source Software adoption in large companies is considered to be a relatively recent movement. Open Source Software is gaining terrain in large organizations, some see it as just another development alternative; others see in it a strategic competitive advantage. In spite of those interests and efforts, techniques available to validate a design against nonfunctional properties, particularly reliability, often require significant expertise unrelated to the usual business of engineering software. As reliability measures quantitatively the quality of correct service delivery, it is probably the most important characteristic for the software engineering discipline 1.2 Overview of Enterprise Resource Planning (ERP) An ERP system is fully integrated business management system covering function area of an enterprise like logistics, finance, accounting, production and human resource. It organize and integrates operation process and information flow to make an optimum use of resources such as Department of industrial engineering and management Page 7
  • 8. R.V. College of Engineering men, material, money and machine. ERP is global, tightly integrated close loop business solution package and it’s multifaceted. ERP promises one database, one application and one user interface for entire enterprise, where once disparate system ruled, manufacturing, distribution, finance and sales. Taking information from every function, it is a tool that assists employee and manager plan, monitor and control the entire business. A modern ERP system enhance a manufacturing ability to accurately schedule production, fully utilize capacity, reduce inventory and meet promised shipping dates. Fig1.1:- General Model of ERP 1.3 Principles of Open Source Architecture 1.3.1 Introduction Open Source Software (OSS) in general refers to any software whose source code is freely available for distribution. The success and benefits of OSS can be attributed to many factors such as code modification by any party as the needs arise, promotion of software reliability and Department of industrial engineering and management Page 8
  • 9. R.V. College of Engineering quality due to peer review and collaboration among many volunteer programmers from different organizations, and the fact that the knowledge-base is not bound to a particular organization, which allows for faster development and the likelihood of the software to be available for different platforms. Characteristics of OSS: It is generally acquired freely. Manufacturer or developer has no right to claim royalties on the distribution or use. Source code is accessible to the user and distributed with the software. No denial to an individual or to a group to access source code of the software. It has provision of modifications and derivations under the programme’s original name. Rights of facilities attached to the programme must not depend on the programme’s being part of a particular software distribution. Licensed software can not place restriction on other software that is distributed with it. Distribution of License should not be specific to a product and License should be technology neutral, etc. 1.3.2 Overview of Apache Open for Business (OFBiz) Apache OFBiz (The Apache Open For Business Project) is a community-driven open source project. For many organizations, OFBiz is also the best e-commerce and enterprise resource planning (ERP) software available. 1. No Licensing Fees: OFBiz is free and open source. 2. Credibility: OFBiz users can rely on the organizational, legal, and financial stability that comes with OFBiz being a top-level project at the Apache Software Foundation (ASF). 3. Collaboration: OFBiz is licensed under the Apache 2.0 open source license, which is both open and business-friendly, facilitating community-driven, meritocratic collaboration while allowing proprietary derivative applications. 4. Flexibility: Users will have complete access to source code it will eliminate "proprietary system" limitations. The entire open source community benefits from making OFBiz as clear, flexible, and reusable as possible. 5. Lower Cost: OFBiz can help user to achieve a system that is as good or better than those available from major proprietary ERP vendors at a significantly lower total project cost. With OFBiz, user can budget for custom features and added value rather than license and maintenance fees. 6. Scalability: Based on the Java platform, OFBiz has the capacity to scale dramatically as needed. Department of industrial engineering and management Page 9
  • 10. R.V. College of Engineering 7. Third Party Friendly: Enjoy freedom from indefinite vendor lock; with OFBiz, user can fully leverage internal resources and/or any of the dozens of organizations offering quality OFBiz implementation and support services. 8. Frequent Updates: Benefit from the active and ongoing contributions of the world-wide OFBiz community. 9. Feature Rich: Leverage standards-based tools and components are attractive to user technology staff, yet already integrated into a common framework. 10. Expert Leadership and Support: HotWax Media provides system design, project planning and management, along with robust technical muscle to deliver the implementation and support necessary for user’s business. 1.3.3 Features of Apache Open for Business (OFBiz) Apache OFBiz applications and brief descriptions Table 1.1: OFBiz applications Accounting Setup your chart of accounts, manage agreements, billing, invoices, Manager payments, and more. Catalog Manager Create catalogs and populate products by categories. Maintain product features, price rules, promotions, subscriptions, reviews, and more. Content Manager Underlying CMS capability that can be customized to manage website content, blogs, surveys, and more. Facility Manager Pick, pack, and ship while maintaining inventory information. Manufacturing MRP, job shop, routing and routing task screens, and BOM screens. Manager Marketing Maintaining mailing lists, manage online marketing campaigns that are Manager fully integrated with e-commerce. Order Manager Manage purchase and sales orders, create orders, handle returns Party Manager Create individuals and groups, manage roles. Web tools Track site traffic and related performance metrics. Application Work Effort Events, calendar, project management, and more ready to be customized Manager to meet your specific needs. 1.3.4 Different Open Source ERP Software’s Enterprise Resource Planning (ERP) is most crucial for a business and they really help business to streamline multisite environment with unified processes across locations. There are so many Department of industrial engineering and management Page 10
  • 11. R.V. College of Engineering proprietary and commercial ERP solutions available like, SAP, Microsoft Dynamics, Oracle e- Business etc. But, very few know that there is a plethora of Free or Low Cost Open Source solutions that are extremely competent as well as with proper customer support just like the proprietary ones. Here is a list of 12 such Open Source ERP/CRM solutions that are either cross platform or browser/web based so that the solutions run on any platforms. All of them have Free versions available without support and some of them offer paid support as optional. a. Compiere: Compiere is a Comprehensive, Adaptable and Low Cost ERP (Java based) solution. Over 1.35 million downloads indicate the proven ERP package. Compiere has support for Cloud Infrastructure, Integration with Sales force and much more. Automate all your business services functions, improve efficiency and customer satisfaction, easy quick customizations, high quality but with low cost ERP suit that will give other competitors a run. b. PostBooks: PostBooks is a full-featured, fully-integrated accounting, ERP, and CRM system, based on the award winning xTuple ERP Suite. Built with the open source PostgreSQL database and the open source Qt framework for C++, it provides the ultimate in power and flexibility for a range of businesses and industries. c. Open taps: Open taps is a full-featured ERP and CRM suite which incorporates several open source projects, including Apache Geronimo, Tomcat, and OFBiz for the data model and transaction framework; Pentaho and Jasper Reports for business intelligence; Funambol for mobile device and Outlook integration; and the Open taps applications which provide user-driven applications for CRM, accounting and finance, warehouse and manufacturing, and purchasing and supply chain management. d. Adempiere: A Java based ERP-System which started as a fork of Compiere, supports a lot of features. A fully fledged ERP, CRM and Supply chain management, Point of Sale suit. e. WebERP: webERP is a complete web based accounting/ERP system that requires only a web-browser and pdf reader to use. It has a wide range of features suitable for many businesses particularly distributed businesses in wholesale and distribution. It is developed as an open-source application and is available as a free download to use. The feature set is continually expanding as new businesses and developers adopt it. Department of industrial engineering and management Page 11
  • 12. R.V. College of Engineering f. BlueERP: BlueERP is a double entry accounting application for small and medium business. Written in PHP, it is delivered through a LAMP environment to provide web access to your accounts. g. Dolibarr: Dolibarr is an ERP/CRM for small and medium companies but also independent or foundations. Dolibarr success is probably due to the 3 simple rules applied on project since the beginning: Easy to install, Easy to use, Easy to develop. Supports lot of features like Supply chain management, proposal management, order management, payment management and much more. h. ERP5: A full featured high end open source ERP designed for better business process, collaboration and leaner management. i. JFire: JFire is a new, powerful and free ERP, CRM, eBusiness and SCM /SRM solution for business enterprises. JFire is entirely free/open-source software, uses the latest Java technologies (EJB 3, JDO 2, Eclipse RCP 3.3) and is designed to be highly customizable. It is a complete and extensible solution that fulfills all your business needs like user management, online trade with business partners, points of sale, various distribution channels forming a distribution network, store management etc. j. OpenERP: OpenERP is open source ERP suit that supports Enterprise modules, Logistics, Accounting and Finance, HRM, CRM, Project Management, Business Process and more. It is complete package with commercial version available. k. Apache OFBiz: The Apache Open for Business Project is an open source enterprise automation software project licensed under the Apache License Version 2.0. As per OFBiz, Open Source enterprise automation means: Open Source ERP, Open Source CRM, Open Source E-Business / E-Commerce, Open Source SCM, Open Source MRP, Open Source CMMS/EAM, and so on. l. OpenBravo: OpenBravo is a web based ERP solution originally was based on Compeiere which is also Open Source. It supports standard ERP features like production information, inventory, customer information, order tracking, and workflow information. Department of industrial engineering and management Page 12
  • 13. R.V. College of Engineering 1.4. Literature Review Parijat Upadhyay et al. made an effort to explore and elaborate the issues in the implementation and reliability of ERP software small and medium scale enterprise (SMEs) with the help of Pareto Analysis since the functionality used at SMEs is not the same as in large scales, hence sacrificing the modules is the secret behind successful implementation of ERP and hence the reliability. [1] Poonam Garg et.al. explains the reasons for failure in implementation of ERP packages in Indian Retail Organizations. The major roadblocks in the implementation of ERP are inadequate resources and poor involvement of end user. ERP is more of a people project then an IT project which cannot be reliably implemented unless there is proper involvement and input by the end user. [2] Mark C. Van Pul et.al. have provided a general overview of theories and processes that is followed to check the reliabilities of a software package. With the help of intensive case studies and mathematical formulas of reliability, it comprehends the fact that no software is complete and even a zillion dollar satellite has bugs. [3] Krešimir Fertalj et al. presented the optimal selection of input parameters that should be done in Software Reliability Growth Model (SRGM). In this particular paper, Weibull model is used to test the reliability of existing installed ERP software and for its further modification. The study elaborates the steps that should be taken by an end user to check the reliability and to predict the trend of the ERP product during its usage. Measurement based analysis represents a good foundation for the future work in modelling of ERP software. [4] F. Urem et.al. attempted to model the probability of bugs in the ERP software after completely upgrading it. The method used is Weibull probability density function (PDF). The up gradation of ERP software, which is a necessity, evolves the complexity of the software and hence induces probability of increasing the bugs unless regular work is done to reduce and maintain it. [5] Department of industrial engineering and management Page 13
  • 14. R.V. College of Engineering Chin-Yu Huang et al. studied the testing effort and the efficience on the modeling of software reliability. The cost of optimal release of the software was another area studied by the author. Testing efficience and the efforts made in testing were the presentation made by the author. [6] Kristine B. Walhovd studied about the sample on which reliability testing for ERP measures were performed comprised of age groups from 21 years to 92 years. The amplitude measures were more reliable at all electrodes as compared to latency measures. The test was done by dividing young and adult age groups into T1 and T2 with a separation of 12-14 months. [7] Sally Wright et al. revealed an understanding of the risks of enterprise resource planning systems (ERP) for consideration in providing information systems assurance services. Increase in the potential for control weaknesses and resulting financial statement errors, inaccurate internal information due to reengineering techniques and customization efforts are explored in the above studies. [8] Yoshinobu Tamura et al. created a fusion of neutral network and software reliability growth model. The author has presented new approach of software reliability growth model. Numerous cases and examples have been quoted to analyze the actual fault count. Then, for open source software the efficiency have been considered in the mention paper. [9] Wangbong Lee et al. They presented as an approach to software reliability assessment of OSS adopted software system in the early stage. It shows the inadequacy of the conventional reliability models to test the reliability of an OSS (Open source software) as OSS can be modified but the COTS (Commercial off The-Shelf) cannot. [10] Swapna S. Gokhale et al. A conventional approach followed in the reliability testing of software is Black Box in which a system is considered as a whole. It is modulated on the basis of interaction with outside world which seems to neglect the inner structure, predicting the reliability of a software system based on its architecture, and the failure behavior of its components, is thus essential. This paper proposes a unifying framework for state-based models for architecture-based software reliability prediction. [11] Department of industrial engineering and management Page 14
  • 15. R.V. College of Engineering Xiaolin Teng et al. The authors have presented a new methodology for predicting software reliability in the field environment. Their work differs from some existing models that assume a constant failure detection rate for software testing and field operation environments, as this new methodology considers the random environmental effects on software reliability. This new methodology provides a viable way to model the user environments, and further makes adjustments to the reliability prediction for similar software products. Based on the generalized software reliability model, further work may include the development of software cost models and the optimum software release policies under random field environments. [12] Table 1.2: Benchmark Reliability Models Models Proposed by Year Type J-M-Model[19] Z. Jelinski Paul and B. Moranda 1972 Binomial G-O-Model[18] Amrit Goel and Kazu Okumoto 1979 Poisson Execution Time Model[20] John Musa 1975 Hyper Exponential Model[21] Ohba 1984 Weibull Model[22] Weibull 1983 Binomial S-Shape Model[23] S. Yamada, M. Ohba, and S.Osaki 1983 Gamma Duane Model[18] J.T Duane 1964 Geometric Model[24] Paul B. Moranda 1979 Binomial Logarithmic Poisson Model[25] Musa –Okumoto 1984 Poisson LV Reliability Growth Model[26] A. Ghaly, P. Chan, & B. Littlewood 1986 Gamma 1.5 Problem Genesis There is currently a need for a creditable end-to-end software reliability model that can be directly linked to reliability prediction from the very beginning (i.e. software design), so as to establish a systematic SRE procedure that can be certified, generalized and refined. The cost of correcting a software error generally increases by magnitudes for every phase of the life cycle. Ideally most of the errors are detected by the end of the unit testing phase. Ideally, the errors found during the integration phase are those due to interfaces that could not have been easily or possibly found during previous phases. Ideally the number of errors detected levels off by the acceptance test phase. If it is known what the average cost of fixing a bug is during each phase of the life cycle, it can be estimated what the cost of repair is and also what it could be. If the Department of industrial engineering and management Page 15
  • 16. R.V. College of Engineering average cost is not known, then the relative cost may be found by comparing real errors detected over time against the ideal. Although, there has been an extensive research work being conducted in the area of software reliability, but there is extremely less work being focused on open source software, especially of Enterprise Resource Planning type, which the current business has high demands. According literature survey many of the previous works on software reliability have used secondary data source for carrying out their research. There is lot of scope for researchers to use primary data source and conduct reliability analysis on open source Enterprise resource planning software. Hence, this poses a high research gap in the area of open source ERP applications in terms of software reliability. 1.6 Objectives  To study on open source ERP software package and understand various business processes.  To study different parameters that affects the reliability of an open source ERP software.  To perform black box testing and identify bugs in open source ERP software packages.  To improve precision of estimation of software architect reliability of ERP system of manufacturing. Department of industrial engineering and management Page 16
  • 17. R.V. College of Engineering 2.0 THEORETICAL CONCEPTS AND FUNDAMENTALS The Institute of Electrical and Electronic Engineers (IEEE) defines software reliability as the probability that software will not cause a system failure for a specified time under specified conditions. The probability is a function of the existence of faults in the software. The inputs to the system determine whether existing faults, if any, are encountered. John Musa of AT&T Bell Laboratories defines software reliability as the probability that a given software system operates for some time period without software error, on the machine for which it was designed, given that it is used within design limits. The measurement and analysis techniques include software metrics, software reliability models, and software analyses such as fault trees and failure modes effects and critically (FMECA). Software metrics are measures of some aspect of the software product or process itself. Software reliability models, for the most part, model the failures occurring because of the software. Software analysis enables development personnel to find errors in the software while the software is still in a laboratory environment. There are at least four major reasons why reliable software has become a very important issue in the last decade or so. 1. Systems are becoming software intensive. Mainly flight systems are becoming more software intensive than hardware intensive. Financial systems including teller, automated teller, and loan processing are software intensive. Defense and energy systems are becoming more software intensive. Everything from insurance rates to credit histories to hotel reservations to long-distance telephone calls is performed by software. Software affects our daily lives. 2. Many software-intensive systems are safety critical. Flight systems, electronic warfare systems, radar, air traffic control, medical systems, energy systems, and space systems are all software-intensive systems that are also safety critical. Even systems that are not safety critical may be mission critical, meaning that success is critical to some end purpose (such as defeating an enemy at war), or failure is extremely costly financially. 3. Customers are requiring more reliable software. Many government contracts are now requiring that an established level of software reliability be achieved. Software has also become part of the system reliability allocations on many government contracts. Commercial clients are also requiring more reliable systems, and many are attempting to Department of industrial engineering and management Page 17
  • 18. R.V. College of Engineering establish the same criteria as the government for developing of reliable software. At one time, software reliability was assumed to be 1 for purposes of determining system reliability. Those days are behind us. 4. Software errors are not being tolerated by end users or by clients of end users. Financial institutions, medical institutions, the government, communication corporations, and other corporations are in a position of being legally liable for software that is not accurate, that causes potential loss of life or loss of mission, that causes inconvenience to end users, and that causes end users to lose profits. In addition to being liable, users and developers of software are also facing increasing maintenance costs. 5. The cost of developing software is increasing. Data from a variety of sources show that for many systems developing the software is becoming one of the major costs of the system, if not the major cost. Much of the software cost can be associated with corrective action, particularly corrective action late in the development cycle. The cost of maintaining software has been shown in some studies to be as much as 40-70% of the total development cost. Some NASA and Air Forces have estimated it to be 50% of their development cost. 2.1 Software Reliability Modeling A software reliability model specifies the general form of the dependence of the failure process on the principal factors that affect it: fault introduction, fault removal, and the operational environment. These models are used to predict how much more time the software needs to be tested to achieve the desired failure intensity and to predict the expected reliability of the delivered software. The model parameters may be determined by means of the following: (1) Estimation: measures the reliability by applying statistical inferences to the collected failure data. This method validates the goodness of the model by assessing its current reliability. (2) Prediction: measures the future software reliability using the available software metrics. The failure data that is used in the reliability models may be of two types: (1) Failure count data which is expressed as the number of failures in each time interval. (2) Time between failure data which is expressed as the time interval between consecutive failures. One type of input data can be transformed to the other to the alternate models either by using the cumulative failure data or by using some of the existing reliability tools such as CASRE and Department of industrial engineering and management Page 18
  • 19. R.V. College of Engineering SMERFS. A well defined software reliability model can determine important characteristics of the failure process by incorporating expressions for the average number of failures experienced at any point in time, the average number of failures in a time interval, the failure intensity at any point in time, and the probability distribution of failure intervals. A good software reliability model, based on sound assumptions, gives better projection of future failure behavior, computes useful quantities, is simple, and is widely applicable. 2.2 Software Reliability Model Classification One of the early reliability models, which was based on hardware reliability concepts, was developed by Duane. In the seventies, many software reliability models were proposed, developed and widely used. Since then, many different software reliability models have been developed and numerous researchers in software reliability engineering have attempted to categorize and classify them. Classify the reliability models in terms of five attributes: (1)Time domain: either calendar or execution time. (2)Category: either finite or infinite number of failures. For the finite number of failures category models, there are a number of classes depending on the functional form of the failure intensity in terms of time. For infinite failure category models, there are a number of families depending on the functional form of the failure intensity in term of the expected number of failures experienced. (3) Type: the distribution of the number of failures experienced as a function of t. (4) Class: functional form of the failure intensity expressed in terms of time (for finite failure category only). (5) Family: functional form of the failure intensity expressed in terms of the expected number of failures experienced (for infinte failure category only). For the sake of simplicity, first separate the finite from the infinite models. Then they incorporate the five attributes as a guide to finding the relationship between the models; thus clarifying the comparison between the models. The simplicity of this classification explains its popularity of usage. Two main categories of software reliability models: (1) software growth reliability models that estimate reliability using the error history. (2) statistical models that estimate the reliability using the results (success or failure) of executing test cases. Department of industrial engineering and management Page 19
  • 20. R.V. College of Engineering The software growth reliability models are classified based on the nature of the failures. The time between failures models, failure counts models, and fault seeding and input domain based models. Categorize models into two major types: (1) Type I : time between successive failures models, which breaks down to failure rate Type I-1 and random function Type I-2. (2) Type II : the number of failures up to a given time. Classify reliability models as follows: (1) Data-domain models: A better reliability estimate can be achieved if all of the combinations of the inputs are identified and the outcomes are well observed. To implement this theory, this model category is decomposed into fault-seeding models and input-domain models. (2) Time- domain models: model the failure process using the failure history to estimate the number of faults and the required test time to uncover these faults. Homogeneous Markov, non- homogeneous Markov, semi-Markov are models that belong to the time-domain model. Classify software reliability according to software development life cycle phases. Their classification is well defined and comprehensive. Table 2.1. Software Reliability Growth Model Examples Model Model μ(t) Reference Comments Name Type Goel-Oku Concave a(l-e-bt) Goel,79 Also called Musa model or moto (G-O) a  0, b>O exponential model G-OS- S-Shaped a( 1-(1+bt)e-bt) Yamada,83 Modification of G-O model to Shaped a  0, b>O make it S-shaped (Gamma function instead of exponential) Hossain- Concave a( l-e-bt)/(1+ce-bt) Hossain,93 Solves a technical condition Dahiya/G- a  0, b>O, c>0 with the G-O model. Becomes O same as G-O as c approaches O. Gompertz S-Shaped a(bct) Kececioglu,, Used by Fujitsu, Numazu a  0, 0  b  1, 0<c<1 91 Works Pareto Concave a(l-(l+t/  )l-  Littlewood, Assumes failures have 81 different failure rates and Department of industrial engineering and management Page 20
  • 21. R.V. College of Engineering a  0,  >0, 0    1 failures with highest rates removed first Weibull Concave a(l-e-btC) Musa,87 Same as G-O for c=1 a  0, b>0,c>0 Yamada Concave a(1-e-r  (1-e-  t)) Yamada,86 Attempts to account for testing Exponential a  0, r  >0,  >0 effort Yamada S-Shaped (  t 2 / 2 ) Yamada,86 Attempts to account for testing a(1  e  r (1e ) ) Raleigh effort Log Infinite (l/c)ln(c  t+l) Musa,87 Failure rate decreases but does Poisson Failure c>O,  >O not approach 0 2.3 Theoretical Comparison of Techniques This section compares the three parameter estimation techniques from a theoretical perspective. We focus on their ease of use, confidence interval shape, and parameter scalability. Since optimization packages are readily available, maximum likelihood, classical least squares, and alternative least squares are all straightforward to solve. However, maximum likelihood only applies to the G-O model, and a new maximum likelihood equation must be derived for each software reliability growth model. These equations can be difficult to derive, especially for the more complex models. Classical least squares applies to the exponential family of models that includes the G-O model. It is fairly easy to modify this equation for similar models. Alternative least squares are the easiest to use since it applies to any software reliability growth model, so the alternative least squares method is the easiest to apply. Confidence intervals for all of the estimation techniques are based on assuming that estimation errors are normally distributed. For the maximum likelihood technique, this assumption is good for large sample sizes because of the asymptotically normal properties of this estimator. However, it is not as good for the smaller samples that we typically have. Nevertheless, the maximum likelihood technique provides the best confidence intervals because it requires less normality assumptions and because it provides asymmetric confidence intervals for the total defect parameter. The lower confidence limit is larger than the number of experienced defects, and the upper confidence limit is farther from the point estimate than the lower confidence limit to represent the possibility that there could be many defects that have gone undetected by testing. Conversely, for the least squares techniques, the lower confidence limit can be less than the number of experienced defects (which is Department of industrial engineering and management Page 21
  • 22. R.V. College of Engineering obviously impossible), and the confidence interval is symmetric. Also, additional assumptions pertaining to the normality of the parameters are necessary to derive confidence intervals for the least squares techniques. The transformation technique consists of multiplying the test time by an arbitrary (but convenient) constant and multiplying the number of defects observed by a different arbitrary constant. For this technique to work, the predicted number of total defects must be unaffected by the test time scaling and must scale the same amount as the defect data. For example, we may experience 50 total defects during test and want to scale that to 100 for confidentiality or ease of reporting. To do that transformation, the number of defects reported each week must be multiplied by 2. If 75 total defects were predicted by a model based on the un scaled data, then the total defects predicted from the scaled data should be 150. Reliability growth models are categorized as hardware models and software models.  Hardware models – using for electronics systems, functions blocks, electronics components without connections to software. Basic terms are a reliability operating state and a failure. There are two basic types of failures during the development phase – random failures, early failures.  Software models –using for software.  Probabilistic reliability growth models – because of no unknown parameters associated with these models, the data obtained during the program cannot be incorporated.  Statistical reliability growth models – unknown parameters are associated with these models. In addition, these parameters are estimated throughout the development of the product in question.  Time independent reliability growth models – number of failures or repairs in definite time interval are not depended on time.  Time dependent reliability growth models – a reliability growth model is function of time.  Continuous reliability growth models – these are time models.  Discrete reliability growth models – these are useful for unrecoverable objects, there are two discrete states – a reliability operating state or a failure.  Classically reliability growth models – mathematical equipment is theory of probability, Duane reliability growth model and its modifications – these are continuous, time dependent and statistical models, Department of industrial engineering and management Page 22
  • 23. R.V. College of Engineering  Stochastic reliability growth models – a reliability growth is non-stationary stochastic process – non-stationary stochastic Poisson process.  Bayes and quasi-Bayes reliability growth models  Unconventional reliability growth models – there are all reliability growth models, for which is no existing possibility to arrange in classification categories. 2.4. Exponential Models All software reliability models of the exponential class have a common set of assumptions. In addition to these common assumptions, each model has its own unique set of assumptions. The standard assumptions are (Lyu 1995): The software is operated in a similar manner as that for which reliability predictions are to be made. This assumption is to ensure that the data collected in that particular environment is applicable to the environment in which the reliability projections are to be made. Every fault has the same chance of being encountered and is of the same severity as any other faults. This assumption is to ensure that the various failures all have the same distributional properties. One severity class might have different failure rates which may require separate reliability analysis. The failures are independent. A failure occurs when the faults are encountered, so having independent failures simplifies the maximum likelihood estimates. Exponential class models have two major types: binomial-type and Poisson-type. In addition to the common assumptions for the exponential class models, the binomial-type models assume that the failures are removed from the software as soon as they occur and introduce no more faults during the fix. The Poisson-type models assume that the faults remaining in the software is a Poisson random variable. The principal difference between the binomial and Poisson type models is how the remaining number of faults are treated. Considering equations (3) and (4), binomial- type models treat the number of remaining faults as a fixed number. While in the Poisson-type models, the number of remaining faults is treated as random variable (Musa, Iannino, Okumoto 1987). Jelinski-Moranda, Musa and Schneidewind models are some the different reliability models. Table 1 summaries the reliability functions for these models. Department of industrial engineering and management Page 23
  • 24. R.V. College of Engineering Table 2.2: Summary of reliability functions for Jelinski-Moranda, Musa and schn. Models Reliability Jelinski-Moranda Musa’a Model Schneidewind’s Functions Model Model F(t) 1  e t 1 e  1t   /   1  e  t  f(t) e t 1e  t e t z(t)  1 e  t 1   /    e  t  1  (t )  N 1  e t   0 1  e   1t   /   1  e  t   (t ) N e t  0 1e   1t e  t Department of industrial engineering and management Page 24
  • 25. R.V. College of Engineering 3.0 METHODOLOGY The following methodology is used to identify error data in the Ofbiz ERP software package. The data collection process is the most critical prerequisites to measuring software reliability. The effectiveness of any reliability measurement will be directly related to the effectiveness of collecting the data necessary for measurement.  Study of Ofbiz ERP software package.  Develop operational profile.  Testing  Data analysis 3.1 Study of Ofbiz ERP software package. The first step in methodology is to thoroughly understand various business processes existing in Ofbiz ERP software package. It is very much essential to understand various business processes before the testing of the software. They are various modules available in Ofbiz software such as catalog manager, bill of materials, purchase module, sales module etc. Key is to understand the integration of all modules. 3.2 Develop operational profile An Operational Profile is simply the set of Operations and their probabilities of occurrence. Most frequently used modules are given more emphasis. More Test cases have to be generated for those modules which are frequently used. In this work the modules which are identified are catalog manager, bill of materials, purchase module and sales module. 3.3 Testing Software testing is an important technique for assessing the quality of software product. Two basic software testing methods are Black Box testing and White Box testing. In this work the software is tested using black box testing. Black Box testing includes performance testing and functionality testing. 3.3.1 Performance testing on different operating systems The performance test is carried to identify how this Ofbiz ERP software package operates on different operating systems such as Windows XP, Windows Vista, Windows 7, Ubuntu. Department of industrial engineering and management Page 25
  • 26. R.V. College of Engineering Table 3.1: Evaluations of Ofbiz in OS Name of Evaluation Evaluation of OFBiz On OS Object of Evaluation OFBiz running on OS on 3GB RAM Test Case Used Execution of OFBiz in 64 bit OS a. Windows XP b. Windows Vista c. Windows 7 Home Basic d. Ubuntu Actual Output In Execution of OFBiz in bit 64Windows professional OS a. The performance is better compared to previous version. But optimum performance varies if RAM is enhanced. b. Performance is much better w.r.t installation, navigation, configuration c. Same as previous versions of Windows. d. Performance is much better compared to windows Remarks Successful performance in windows XP , Vista, 7 and Ubuntu 3.3.2 Performance testing on different web browsers. After testing the performance of software on different operating systems next step is to test Ofbiz software performance on different web browsers. Mozilla Firefox, Internet Explorer and Google Chrome are identified browsers for testing. Department of industrial engineering and management Page 26
  • 27. R.V. College of Engineering Table 3.2: Ofbiz performance for Browser Name of Testing OFBiz Testing OFBiz Testing OFBiz Evaluation performance on Mozilla performance on Internet performance on Google Firefox version 13.0 Explorer 9 Chrome Object of Firefox performance with IE-9.0 performance with Google Chrome Evaluation OFBiz OFBiz performance with OFBiz Test Case 1. Navigation of 5. Navigation of 9. Navigation of Used pages pages pages 2. Cookies 6. Cookies Clearance 10. Cookies Clearance Clearance 7. History Scrutiny 11. History Scrutiny 3. History Scrutiny 8. Page Visualization 12. Page Visualization 4. Page Visualization Actual 1. Navigation of 5. Navigation of 9. Navigation of Output Pages are very Pages are very pages are quite smooth smooth faster 2. Proper cookie 6. Cookies are not 10. In Google chrome, management managed properly incognito window 3. Proper history and 7. No proper history enables the better cache and cache cookies clearance. management management 11. Better history 4. The pages are 8. The pages are management due visually better to visually not so to incognito experience better to window. experience as user 12. The pages visualization is better compare to internet explorer 9 Remarks Mozilla Firefox seems to IE-9.0 seems not to be best Google chrome is also be best compatible with compatible with OFBiz found to be best OFBiz compatible browser after Firefox Mozilla. Department of industrial engineering and management Page 27
  • 28. R.V. College of Engineering 3.4 Data collection Software testing is an important technique for assessing the quality of software product. Two basic software testing are Black Box testing and White Box testing. Black Box testing includes performance testing and functionality testing. In performance testing, data is collected on basis of working of modules in different browsers in single operating system and also in different operating system. From the study found that software failure follows exponential distribution, for example suppose we test to failure a large number of software operation, for each small unit of time, if we calculate the failure rate and plot the same against time t, the resulting graph is the failure rate curve. The value of reliability R(t) is 1 at t = 0 and it decreases continuously with time when (t) is large, all the software will have failed giving R(t) = 0 at t ∞ It can be proved that in case of constant failure rate, R= λ = constant failure rate T = mission time. We conducted experiment on oFbiz open source software creating bill of material ,purchase order, sales order and purchase order through MRP run are some of certain operations which has been frequently used in many organizations. By creating bill of material and time is noted down, these has been repeated for ten times and tabulated, average time of ten reading have calculated and standard time has been found, standard time is sum of average time and 10% of average time. Then based on standard time failure rate (λ) is calculated, failure rate is number of time creating a bill of material exceeded standard time, same Department of industrial engineering and management Page 28
  • 29. R.V. College of Engineering procedure is been carried to other ERP modules and then the reliability of software in particular web browser for particular operating system is calculated. Standard time = Basic average time + (10%* Basic average time) Failure rate (λ) = No. Of operations exceeds standard time/total no. of trails 3.4.1 Performance testing of different modules of Ofbiz on different Web browsers. The important modules which are identified in operational profile stage are being tested on different web browsers. Time taken to create bill of materials, purchase order, sales order and purchase order through MRP are noted down for different web browsers. Readings of 10 trails for different browsers with different operating system for processing time of different operations shown in below tables :- Table 3.3: Windows XP –Internet Explorer Purchase order Bill of Purchase Sales through Sl.no material order order MRP 1 9.3 4.2 4.2 5.6 2 9 4.1 4 5.4 3 8.9 3.8 3.8 5.1 4 8.4 3.7 3.5 4.9 5 8.2 3.5 3.4 4.6 6 8.1 3.4 3.1 4.4 7 7.9 3.2 2.8 4.2 8 7.8 2.9 2.7 4 9 7.7 2.6 2.3 3.9 10 7.7 2.6 2.2 3.9 sum 83 34 32 46 Avg 8.3 3.4 3.2 4.6 Note all readings are in minutes Department of industrial engineering and management Page 29
  • 30. R.V. College of Engineering Table 3.4: Windows XP –Mozilla Firefox Purchase order Bill of Purchase Sales through Sl.no material order order MRP 1 9.3 4.3 4.3 5.6 2 9 4.2 4.1 5.4 3 8.9 3.9 3.9 5.1 4 8.4 3.8 3.6 4.9 5 8.2 3.6 3.5 4.6 6 8.1 3.5 3.2 4.4 7 7.9 3.3 2.9 4.2 8 7.8 3 2.8 4 9 7.7 2.7 2.4 3.9 10 7.7 2.7 2.3 3.9 sum 83 35 33 46 Avg 8.3 3.5 3.3 4.6 Note all readings are in minutes. Table 3.5: Windows XP –Google Chrome Purchase order Bill of Purchase Sales through Sl.no material order order MRP 1 9.2 4.3 4.3 5.5 2 8.9 4.2 4.1 5.3 3 8.8 3.9 3.9 5 4 8.3 3.8 3.6 4.8 5 8.1 3.6 3.5 4.5 6 8 3.5 3.2 4.3 7 7.8 3.3 2.9 4.1 8 7.7 3 2.8 3.9 9 7.6 2.7 2.4 3.8 10 7.6 2.7 2.3 3.8 sum 82 35 33 45 Avg 8.2 3.5 3.3 4.5 Note all readings are in minutes. Department of industrial engineering and management Page 30
  • 31. R.V. College of Engineering Table 3.6: Windows Vista – Internet Explorer Purchase order Bill of Purchase Sales through Sl.no material order order MRP 1 9.3 4 4.5 5.7 2 9 3.9 4.3 5.6 3 8.7 3.6 4.1 5.4 4 8.6 3.5 3.8 5.1 5 8.5 3.3 3.7 4.8 6 8.3 3.2 3.4 4.6 7 8.2 3 3.1 4.4 8 8.2 2.7 3 4.2 9 8.1 2.4 2.6 4.1 10 8.1 2.4 2.5 4.1 sum 85 32 35 48 Avg 8.5 3.2 3.5 4.8 Note all readings are in minutes. Table3.7: Windows Vista –Mozilla Firefox Purchase Bill of Purchase Sales order through Sl.no material order order MRP 1 9.6 4.2 4.6 6 2 9.4 4.1 4.4 5.9 3 9.2 3.8 4.2 5.7 4 8.9 3.7 3.9 5.4 5 8.6 3.5 3.8 5.1 6 8.5 3.4 3.5 4.9 7 8.3 3.2 3.2 4.7 8 8.2 2.9 3.1 4.5 9 8.1 2.6 2.7 4.4 10 8.2 2.6 2.6 4.4 sum 87 34 36 51 Avg 8.7 3.4 3.6 5.1 Note all readings are in minutes. Department of industrial engineering and management Page 31
  • 32. R.V. College of Engineering Table 3.8: Windows Vista –Google Chrome Purchase Purchase Sales order through Sl.no Bill of material order order MRP 1 9.6 4.3 4.3 5.9 2 9.3 4.2 4.1 5.8 3 9.2 3.9 3.9 5.6 4 8.7 3.8 3.6 5.2 5 8.5 3.6 3.5 5.1 6 8.4 3.5 3.2 4.8 7 8.2 3.3 2.9 4.7 8 8.1 3 2.8 4.3 9 8 2.7 2.4 4.3 10 8 2.7 2.3 4.3 sum 86 35 33 50 Avg 8.6 3.5 3.3 5 Note all readings are in minutes. Table 3.9: Windows 7 – Google Chrome. Purchase order Purchase Sales through Sl.no Bill of material order order MRP 1 9.5 3.8 4.2 5.1 2 9.2 3.7 4 4.8 3 9.1 3.4 3.8 4.7 4 8.6 3.3 3.5 4.6 5 8.4 3.1 3.4 4.4 6 8.3 3 3.1 4.2 7 8.1 2.8 2.8 3.7 8 8 2.5 2.7 3.6 9 7.9 2.2 2.3 3.5 10 7.9 2.2 2.2 3.4 sum 85 30 32 42 Avg 8.5 3 3.2 4.2 Note all readings are in minutes. Department of industrial engineering and management Page 32
  • 33. R.V. College of Engineering Table 3.10: Windows 7 – Mozilla Firefox Purchase order Purchase Sales through Sl.no Bill of material order order MRP 1 8.9 4.3 4.7 5.4 2 8.7 4.2 4.5 5.1 3 8.6 3.9 4.3 5 4 8.1 3.8 4 4.9 5 7.9 3.6 3.9 4.7 6 7.8 3.5 3.6 4.5 7 7.6 3.3 3.3 4 8 7.5 3 3.2 3.9 9 7.5 2.7 2.8 3.8 10 7.4 2.7 2.7 3.7 sum 80 35 37 45 Avg 8 3.5 3.7 4.5 Note all readings are in minutes. Table 3.11: Windows 7 – Internet Explorer Purchase order Purchase Sales through Sl.no Bill of material order order MRP 1 8.5 3.3 3.6 4.3 2 8.3 3.2 3.4 3.8 3 8.3 2.9 3.2 3.7 4 7.7 2.8 2.9 3.6 5 7.5 2.6 2.8 3.4 6 7.4 2.5 2.5 3.3 7 7.2 2.3 2.2 2.7 8 7.1 2 2.1 2.5 9 7.1 1.7 1.7 2.4 10 6.9 1.7 1.6 2.3 sum 76 25 26 32 Avg 7.6 2.5 2.6 3.2 Note all readings are in minutes. Department of industrial engineering and management Page 33
  • 34. R.V. College of Engineering Table 3.12: Ubuntu– Internet Explorer Firefox Bill of Sl.no material Sales order Purchase Order 1 8.2 3.2 3.2 2 8.1 3 3.1 3 8 2.9 2.9 4 7.6 2.7 2.5 5 7.4 2.6 2.5 6 7.2 2.5 2.3 7 7.1 2.1 2.2 8 7 1.9 1.9 9 6.8 1.7 1.7 10 6.6 1.4 1.7 Sum 74 24 24 Avg 7.4 2.4 2.4 Note all readings are in minutes. Table 3.13:Ubutu– Internet Explorer Firefox Bill of Sales Purchase Sl.no material order Order 1 8.4 4 3.9 2 8.3 3.8 3.8 3 8.2 3.7 3.6 4 7.8 3.5 3.2 5 7.6 3.4 3.2 6 7.4 3.3 3 7 7.3 2.9 2.9 8 7.2 2.7 2.6 9 7 2.5 2.4 10 6.8 2.2 2.4 Sum 76 32 31 Avg 7.6 3.2 3.1 Note all readings are in minutes. Department of industrial engineering and management Page 34
  • 35. R.V. College of Engineering Table 3.14 : Ubuntu– Internet Explorer Firefox Bill of Sales Sl.no material order Purchase Order 1 8.3 3.7 3.7 2 8.2 3.5 3.6 3 8.1 3.4 3.4 4 7.7 3.2 3 5 7.5 3.1 3 6 7.3 3 2.8 7 7.2 2.6 2.7 8 7.1 2.4 2.4 9 6.9 2.2 2.2 10 6.7 1.9 2.2 Sum 75 29 29 Avg 7.5 2.9 2.9 Note all readings are in minutes. 3.4.2 Functionality Testing Functional testing is a type of black box testing that bases its test cases on the specifications of the software component under test. Functions are tested by feeding them input and examining the output, and internal program structure is rarely considered. Functional testing differs from system testing in that functional testing " a program by checking it against design document or specification", while system testing " a program by checking it against the published user or system requirements" Functional testing typically involves five steps:- 1. The identification of functions that the software is expected to perform 2. The creation of input data based on the function's specifications 3. The determination of output based on the function's specifications 4. The execution of the test case 5. The comparison of actual and expected outputs Department of industrial engineering and management Page 35
  • 36. R.V. College of Engineering 3.4.2.1 TEST CASES Table 3.15: Test cases Step. Step Description Test Data Actions Expected Actual Status no Results Result T01 Examine entering Business Click on Loged-in Loged-in Pass username & 12345 Login password T02 Examine entering Business Click on Pop-up box Pop-up box Pass correct username 12366 Login & wrong password T03 Examine entering Business Click on Pop-up box Pop-up box Pass wrong username 12345 Login & correct password T04 Examine entering Business Click on Pop-up box Pop-up box Pass wrong username 12356 Login & wrong password T05 Ensure after log-in Business Click on Open Open Pass should go to 12345 Login homepage homepage homepage T06 Check BOM on Create all Click on BOM pdf BOM pdf Pass screen product BOM give price, simulation supplier and facilities T07 Check BOM on Create Click on Should not Should not Pass screen product BOM show BOM show BOM Department of industrial engineering and management Page 36
  • 37. R.V. College of Engineering Without simulation giving facilities T08 Examine sales Input Click on Should Should give Pass enquiry enquiry supplier give supplier number supplier sales sales T09 Examine product Input Click on Display Display Pass detail product product product product name/ID detail detail detail T10 Examine entering Id: cap Click on Show error Show error Pass same product id & name: cap create that that product name product duplicate duplicate key arise key arise T11 Examine entering Id: cap Click on Product Product Pass different product name: caps create created created id & product name product T12 Edit product detail Product Click on Go to edit Go to edit Pass detail edit screen screen product T13 Examine product Engine Click on Pop-up box Pop-up box Pass ID lathe create product T14 Examine product Engine Click on Product Product Pass ID without space _lathe create created created product T15 Examine product Without Click on Pop-up box Pop-up box Pass without internal internal create name name product T16 Examine product With Click on Pop-up box Pop-up box Pass with internal name internal create Department of industrial engineering and management Page 37
  • 38. R.V. College of Engineering name product T17 Creating suppliers Product Click on Pop-u box Pop-u box Pass for subassemblies id:__ create without product id Product & name name:__ T18 Creating suppliers Product Click on Pop-u box Pop-u box Pass for subassemblies id:chuck9 create with product id & Product name name: chuck T19 Changing Indian Click on Not Not Pass currency in price rupee to edit applicable applicable module USD product price update T20 Creating Select Click on Should Should Pass manufacturing facility has BOM show all show all BOM manufactu simulation elements element ring BOM T21 Creating Select Click on Should Should Pass engineering BOM facility has BOM show only show only engineerin simulation parent parent g BOM element element T22 Examine without Create Click on Pop-up box Pop-up box pass selecting shipping sales or finalize address purchase order order without selecting address T23 Examine selecting Create Click on Sales order Sales order Pass Department of industrial engineering and management Page 38
  • 39. R.V. College of Engineering shipping address sales or finalize or purchase or purchase purchase order order is order is order with created created selecting address T24 Examine payment Create Select Should Should Pass mode sales or shipping show the show the purchase address payment payment order with mode mode selecting address T25 Examine payment Create without Should not Should not Pass mode sales or Selecting show the show the purchase shipping payment payment order address mode mode without selecting address 3.4.2.2 Ofbiz Defect Tracker 1. Problem Description While creating sales order through order entry, how shall we ship it? Mode for USPS standard shown calculated offline. This is to be selected but by selecting will not allow us to carry out the sales order. Bug severity:- Minor Found By :- Managing Maniac Date :- 2-4-2012 Steps to Reproduce • Go to application-select order manager • Select order entry-give product ID and click on add to order Department of industrial engineering and management Page 39
  • 40. R.V. College of Engineering • Finalize the order-select the shipping address-shipping mode with USPS standard-payment mode • Click on continue to finalize order and it should show the order conformation page. Fig.3.1: Quick finalize order Work Around Sales order is created using order entry in order manager, by selecting any other shipping mode and then click on continue to final order after creating order we can change the shipping mode to USPS standard by updating the shipping information. 2. Problem description When a Purchase Order is received manually using the ‘receive functionality’ of Facility Manager module, the invoice entry in accounting is not automatically raised. Bug severity:- Minor Found by :- Managing Maniac Date:- 6-4-2012 Department of industrial engineering and management Page 40
  • 41. R.V. College of Engineering Fig. 3.2: Shows how to receive quick purchase order Steps to reproduce • Create a purchase order using the order entry screen of Order Manager • Once the order is created, approve the purchase order • In the Facility Manager, select the facility as Kamakshi Palya Plant and receive the inventory using ‘Receive Inventory’ option • Once receive inventory is successful, check the status of the PO in Order Manager. It will be shown as ‘Complete’ • Check for a Purchase Order Invoice entry in accounting module using the functionality ‘Find Purchase Invoice’. There should be a PO invoice corresponding to the amount of the PO fulfilled. Department of industrial engineering and management Page 41
  • 42. R.V. College of Engineering Fig.3.3: Finding Invoice Work Around – When a purchase order is created using the order entry functionality of Order manager, there is an option to quick receive purchase order. When a PO is received using this option the purchase order invoice entry is made automatically. Shown below is the screenshot of Quick receive PO functionality. 3. Problem description When a Purchase Order is raised and approved, in the Quick Receive option, newly created facility is not listed. Bug Severity – Minor, data related issue Found by – Managing Maniac Date – 10-4-2012 Steps to reproduce – • Create a new facility ‘Kamakshi palya Plant’ of facility type ‘Plant’ • During the creation of the PO, in the order entry ship to settings, though the newly created plant is listed, there is no option to select it. Department of industrial engineering and management Page 42
  • 43. R.V. College of Engineering Fig.3.4: Order Entry Work Around-In the Facility Manager, select the facility as Kamakshi Palya Plant and create a contact mechanism of type ‘Postal Address’ and save the details Fig.3.5: Facility Manager Department of industrial engineering and management Page 43
  • 44. R.V. College of Engineering Try to create the purchase order again, as you can see in the screenshot below the newly created plant is visible as an option to be selected for shipment Fig.3. 6: Shipping addresses update 4. Problem description – When creating a Purchase Order, in the ‘Add Item’ screen the top menu bar is not clearly visible as shown in the screenshot below Bug Severity – Low, Cosmetic Found by – Managing Maniac Date – 21-4-2012 Steps to reproduce • Create a purchase order using the order entry screen • Select the supplier and click continue Department of industrial engineering and management Page 44
  • 45. R.V. College of Engineering • Enter the order name and order number and click continue • In the add items screen displayed below the text is not clearly visible in top menu Fig.3.7: Screen Visuality Files Modified: ofbiz-trunkapplicationsorderwebappordermgrentryOrderEntryTabBar.ftl Verification: After fixing the defect, it can be seen that the options in top menu bar are clearly visible. Department of industrial engineering and management Page 45
  • 46. R.V. College of Engineering Fig.3. 8: Screen after solving 3.5 Data Analysis Analysis of data is a process of inspecting, cleaning, transforming, and modelling data with the goal of highlighting useful information, suggesting conclusions, and supporting decision making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science domains. Data mining is a particular data analysis technique that focuses on modelling and knowledge discovery for predictive rather than purely descriptive purposes. Business intelligence covers data analysis that relies heavily on aggregation, focusing on business information. In statistical applications, some people divide data analysis into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA). EDA focuses on discovering new features in the data and CDA on confirming or falsifying existing hypotheses. Predictive Department of industrial engineering and management Page 46
  • 47. R.V. College of Engineering analytics focuses on application of statistical or structural models for predictive forecasting or classification, while text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a species of unstructured data. All are varieties of data analysis. Data integration is a precursor to data analysis, and data analysis is closely linked to data visualization and data dissemination. The term data analysis is sometimes used as a synonym for data modelling Analysis is done by above collected data as follow:- For Operating System : Windows XP Fig.3.9: Comparison graph in windows XP Department of industrial engineering and management Page 47
  • 48. R.V. College of Engineering Fig.3.10: Avg. time graph in windows XP Basic Avg. Time =No. of trials/N Basic Avg. Time = (9.3+9+8.9+8.4+8.2+8.1+7.9+7.8+7.7+7.7)/10 Basic Avg. Time = 8.3 minutes Standard Time = Basic Avg. Time * 10% allowance of basic Avg. Time Standard Time = 8.3 * (8.3*0.1) Standard Time = 9.13 minutes Reliability(R) = R= e -0.4*1 = 0.6703 Avg.reliability = (R1 + R2+ R3)/n Avg.reliability = 0.7173 Avg.reliability ==71.73% Department of industrial engineering and management Page 48