Software Reliability Growth Models (SRGM) have been developed to estimate software reliability measures such as
software failure rate, number of remaining faults and software reliability. In this paper, the software analyzers tool proposed
for deriving several software reliability growth models based on Enhanced Non-homogeneous Poisson Process (ENHPP) in
the presence of imperfect debugging and error generation. The proposed models are initially formulated for the case when
there is no differentiation between failure observation and fault removal testing processes and then this extended for the case
when there is a clear differentiation between failure observation and fault removal testing processes. Many Software
Reliability Growth Models (SRGM) have been developed to describe software failures as a random process and can be used
to measure the development status during testing. With SRGM software consultants can easily measure (or evaluate) the
software reliability (or quality) and plot software reliability growth charts.
Flexibility a key factor to testability ijseajournal
Testability is an important software quality factor that is ineffective if it is not available at an early stage in
the development life-cycle. It becomes more essential in the case of object oriented design. Flexibility is an
important key factor to testability analysis and measurement for delivering high class testable and
maintainable software. Flexibility is a criterion of crucial significance to software developers, designers
and the quality controllers. It constantly guides and supports to avoid wastage of resources as well as
enable the designers for continuous improvement in the development process. Flexibility is concerned with
building high quality and reliable software within the constraints of cost and time. It greatly influences
cost, quality and reliability at software evolution process. Despite the fact flexibility is vital and highly
significant aspect for software development processes, it is poorly managed. This paper focuses the need
and importance of flexibility early at design phase. A model has been proposed for flexibility measurement
of object oriented design by establishing multiple linear regression. Finally the proposed model has been
validated using experimental tryout.
Software Testing and Quality Assurance Assignment 3Gurpreet singh
Short questions :
Que 1 : Define Software Testing.
Que 2 : What is risk identification ?
Que 3 : What is SCM ?
Que 4 : Define Debugging.
Que 5 : Explain Configuration audit.
Que 6 : Differentiate between white box testing & black box testing.
Que 7 : What do you mean by metrics ?
Que 8 : What do you mean by version control ?
Que 9 : Explain Object Oriented Software Engineering.
Que 10 : What are the advantages and disadvantages of manual testing tools ?
Long Questions:
Que 1 : What do you mean by baselines ? Explain their importance.
Que 2 : What do you mean by change control ? Explain the various steps in detail.
Que 3 : Explain various types of testing in detail.
Que 4 : Differentiate between automated testing and manual testing.
Que 5 : What is web engineering ? Explain in detail its model and features.
ANALYSIS OF SOFTWARE QUALITY USING SOFTWARE METRICSijcsa
Software metrics have a direct link with measurement in software engineering. Correct measurement is the prior condition in any engineering fields, and software engineering is not an exception, as the size and complexity of software increases, manual inspection of software becomes a harder task. Most Software Engineers worry about the quality of software, how to measure and enhance its quality. The overall objective of this study was to asses and analysis’s software metrics used to measure the software product and process.
In this Study, the researcher used a collection of literatures from various electronic databases, available since 2008 to understand and know the software metrics. Finally, in this study, the researcher has been identified software quality is a means of measuring how software is designed and how well the software conforms to that design. Some of the variables that we are looking for software quality are Correctness, Product quality, Scalability, Completeness and Absence of bugs, However the quality standard that was used from one organization is different from others for this reason it is better to apply the software metrics to measure the quality of software and the current most common software metrics tools to reduce the subjectivity of faults during the assessment of software quality. The central contribution of this study is an overview about software metrics that can illustrate us the development in this area, and a critical analysis about the main metrics founded on the various literatures.
The key to successful testing is effective and timely planning. Rick Craig introduces proven test planning methods and techniques, including the Master Test Plan and level-specific test plans for acceptance, system, integration, and unit testing. Rick explains how to customize an IEEE-829-style test plan and test summary report to fit your organization’s needs. Learn how to manage test activities, estimate test efforts, and achieve buy-in. Discover a practical risk analysis technique to prioritize your testing and become more effective with limited resources. Rick offers test measurement and reporting recommendations for monitoring the testing process. Discover new methods and develop renewed energy for taking your organization’s test management to the next level.
Flexibility a key factor to testability ijseajournal
Testability is an important software quality factor that is ineffective if it is not available at an early stage in
the development life-cycle. It becomes more essential in the case of object oriented design. Flexibility is an
important key factor to testability analysis and measurement for delivering high class testable and
maintainable software. Flexibility is a criterion of crucial significance to software developers, designers
and the quality controllers. It constantly guides and supports to avoid wastage of resources as well as
enable the designers for continuous improvement in the development process. Flexibility is concerned with
building high quality and reliable software within the constraints of cost and time. It greatly influences
cost, quality and reliability at software evolution process. Despite the fact flexibility is vital and highly
significant aspect for software development processes, it is poorly managed. This paper focuses the need
and importance of flexibility early at design phase. A model has been proposed for flexibility measurement
of object oriented design by establishing multiple linear regression. Finally the proposed model has been
validated using experimental tryout.
Software Testing and Quality Assurance Assignment 3Gurpreet singh
Short questions :
Que 1 : Define Software Testing.
Que 2 : What is risk identification ?
Que 3 : What is SCM ?
Que 4 : Define Debugging.
Que 5 : Explain Configuration audit.
Que 6 : Differentiate between white box testing & black box testing.
Que 7 : What do you mean by metrics ?
Que 8 : What do you mean by version control ?
Que 9 : Explain Object Oriented Software Engineering.
Que 10 : What are the advantages and disadvantages of manual testing tools ?
Long Questions:
Que 1 : What do you mean by baselines ? Explain their importance.
Que 2 : What do you mean by change control ? Explain the various steps in detail.
Que 3 : Explain various types of testing in detail.
Que 4 : Differentiate between automated testing and manual testing.
Que 5 : What is web engineering ? Explain in detail its model and features.
ANALYSIS OF SOFTWARE QUALITY USING SOFTWARE METRICSijcsa
Software metrics have a direct link with measurement in software engineering. Correct measurement is the prior condition in any engineering fields, and software engineering is not an exception, as the size and complexity of software increases, manual inspection of software becomes a harder task. Most Software Engineers worry about the quality of software, how to measure and enhance its quality. The overall objective of this study was to asses and analysis’s software metrics used to measure the software product and process.
In this Study, the researcher used a collection of literatures from various electronic databases, available since 2008 to understand and know the software metrics. Finally, in this study, the researcher has been identified software quality is a means of measuring how software is designed and how well the software conforms to that design. Some of the variables that we are looking for software quality are Correctness, Product quality, Scalability, Completeness and Absence of bugs, However the quality standard that was used from one organization is different from others for this reason it is better to apply the software metrics to measure the quality of software and the current most common software metrics tools to reduce the subjectivity of faults during the assessment of software quality. The central contribution of this study is an overview about software metrics that can illustrate us the development in this area, and a critical analysis about the main metrics founded on the various literatures.
The key to successful testing is effective and timely planning. Rick Craig introduces proven test planning methods and techniques, including the Master Test Plan and level-specific test plans for acceptance, system, integration, and unit testing. Rick explains how to customize an IEEE-829-style test plan and test summary report to fit your organization’s needs. Learn how to manage test activities, estimate test efforts, and achieve buy-in. Discover a practical risk analysis technique to prioritize your testing and become more effective with limited resources. Rick offers test measurement and reporting recommendations for monitoring the testing process. Discover new methods and develop renewed energy for taking your organization’s test management to the next level.
Introduction to Investigation And Utilizing Lean Test Metrics In Agile Softwa...IJERA Editor
The growth of the software development industry approaches the new development methodologies to deliver the
error free software to its end-user fulfilling the business values to product. The growth of tools and technology
has brought the automation in the development and software testing process, it has also increased the demand of
the fast testing and delivery of the software to end customers. Traditional software development methodologies
to trending agile software development trend have brought new philosophy, dimensions, and processes having
invested new tools to make process easy. The Agile development (Scrum, XP, FDD, BDD, ATDD, ASD,
DSDM, Kanban, Crystal and Lean) process also faces the software testing model crises because of the fast
development of life cycles, fast delivery to end users without having appropriate test metrics which make the
software testing process slow as well as increase the risk. The analysis of the testing metrics in the software
testing process and setting the right lean test metrics help to improve the software quality effectively in agile
process.
This is chapter 6 of ISTQB Advance Technical Test Analyst certification. This presentation helps aspirants understand and prepare the content of the certification.
Chapter 6 - Transitioning Manual Testing to an Automation EnvironmentNeeraj Kumar Singh
This is the chapter 6 of ISTQB Advance Test Automation Engineer certification. This presentation helps aspirants understand and prepare content of certification.
This is chapter 4 of ISTQB Advance Agile Technical Tester certification. This presentation helps aspirants understand and prepare the content of the certification.
This is chapter 2 of ISTQB Advance Test Manager certification. This presentation helps aspirants understand and prepare the content of the certification.
Introduction to Investigation And Utilizing Lean Test Metrics In Agile Softwa...IJERA Editor
The growth of the software development industry approaches the new development methodologies to deliver the
error free software to its end-user fulfilling the business values to product. The growth of tools and technology
has brought the automation in the development and software testing process, it has also increased the demand of
the fast testing and delivery of the software to end customers. Traditional software development methodologies
to trending agile software development trend have brought new philosophy, dimensions, and processes having
invested new tools to make process easy. The Agile development (Scrum, XP, FDD, BDD, ATDD, ASD,
DSDM, Kanban, Crystal and Lean) process also faces the software testing model crises because of the fast
development of life cycles, fast delivery to end users without having appropriate test metrics which make the
software testing process slow as well as increase the risk. The analysis of the testing metrics in the software
testing process and setting the right lean test metrics help to improve the software quality effectively in agile
process.
This is chapter 6 of ISTQB Advance Technical Test Analyst certification. This presentation helps aspirants understand and prepare the content of the certification.
Chapter 6 - Transitioning Manual Testing to an Automation EnvironmentNeeraj Kumar Singh
This is the chapter 6 of ISTQB Advance Test Automation Engineer certification. This presentation helps aspirants understand and prepare content of certification.
This is chapter 4 of ISTQB Advance Agile Technical Tester certification. This presentation helps aspirants understand and prepare the content of the certification.
This is chapter 2 of ISTQB Advance Test Manager certification. This presentation helps aspirants understand and prepare the content of the certification.
An advisory firm delivering services to the investors may help you in this sector. They use to provide such professionals who give such tips and hints which benefits the traders and help them to achieve the desired success.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
Determination of Software Release Instant of Three-Tier Client Server Softwar...Waqas Tariq
Quality of any software system mainly depends on how much time testing take place, what kind of testing methodologies are used, how complex the software is, the amount of efforts put by software developers and the type of testing environment subject to the cost and time constraint. More time developers spend on testing more errors can be removed leading to better reliable software but then testing cost will also increase. On the contrary, if testing time is too short, software cost could be reduced provided the customers take risk of buying unreliable software. However, this will increase the cost during operational phase since it is more expensive to fix an error during operational phase than during testing phase. Therefore it is essentially important to decide when to stop testing and release the software to customers based on cost and reliability assessment. In this paper we present a mechanism of when to stop testing process and release the software to end-user by developing a software cost model with risk factor. Based on the proposed method we specifically address the issues of how to decide that we should stop testing and release the software based on three-tier client server architecture which would facilitates software developers to ensure on-time delivery of a software product meeting the criteria of achieving predefined level of reliability and minimizing the cost. A numerical example has been cited to illustrate the experimental results showing significant improvements over the conventional statistical models based on NHPP.
ANALYSIS OF SOFTWARE QUALITY USING SOFTWARE METRICSijcsa
Software metrics have a direct link with measurement in software engineering. Correct measurement is the prior condition in any engineering fields, and software engineering is not an exception, as the size and complexity of software increases, manual inspection of software becomes a harder task. Most Software Engineers worry about the quality of software, how to measure and enhance its quality. The overall objective of this study was to asses and analysis’s software metrics used to measure the software product and process.
In this Study, the researcher used a collection of literatures from various electronic databases, available since 2008 to understand and know the software metrics. Finally, in this study, the researcher has been identified software quality is a means of measuring how software is designed and how well the software conforms to that design. Some of the variables that we are looking for software quality are Correctness, Product quality, Scalability, Completeness and Absence of bugs, However the quality standard that was used from one organization is different from others for this reason it is better to apply the software metrics to measure the quality of software and the current most common software metrics tools to reduce the subjectivity of faults during the assessment of software quality. The central contribution of this study is an overview about software metrics that can illustrate us the development in this area, and a critical analysis about the main metrics founded on the various literatures.
11 steps of testing process - By Harshil BarotHarshil Barot
11 Steps of The Software Testing Process.Software Testing Process is a Find out the Maximum Bugs and Errors From the Software or Product and Make the Software
Bugs or Error Free.(Bugs/Errors/Defects).
STATISTICAL ANALYSIS OF METRICS FOR SOFTWARE QUALITY IMPROVEMENT ijseajournal
Software product quality can be defined as the features and characteristics of the product that meet the user needs. The quality of any software can be achieved by following a well defined software process. These software process results into various metrics like Project metrics, Product metrics and Process metrics. Software quality depends on the process which is carried out to design and develop software. Even though the process can be carried out with utmost care, still it can introduce some error and defects. Process metrics are very useful from management point of view. Process metrics can be used for improving the software development and maintenance process for defect removal and also for reducing the response
time.
This paper describes the importance of capturing the Process metrics during the quality audit process and also attempts to categorize them based on the nature of error captured. To reduce such errors and defects found, steps for corrective actions are recommended.
Software testing is an activity which is aimed for evaluating quality of a program and also for improving it, by identifying defects and problems. Software testing strives for achieving its goal (both implicit and explicit) but it has certain limitations, still testing can be done more effectively if certain established principles are to be followed. In spite of having limitations, software testing continues to dominate other verification techniques like static analysis, model checking and proofs. So it is indispensable to understand the goals, principles and limitations of software testing so that the effectiveness of software testing could be maximized.
A survey of predicting software reliability using machine learning methodsIAESIJAI
In light of technical and technological progress, software has become an urgent need in every aspect of human life, including the medicine sector and industrial control. Therefore, it is imperative that the software always works flawlessly. The information technology sector has witnessed a rapid expansion in recent years, as software companies can no longer rely only on cost advantages to stay competitive in the market, but programmers must provide reliable and high-quality software, and in order to estimate and predict software reliability using machine learning and deep learning, it was introduced A brief overview of the important scientific contributions to the subject of software reliability, and the researchers' findings of highly efficient methods and techniques for predicting software reliability.
From previous year researches, it is concluded that testing is playing a vital role in the development of the software product. As, software testing is a single approach to assure the quality of the software so most of the development efforts are put on the software testing. But software testing is an expensive process and consumes a lot of time. So, testing should be start as early as possible in the development to control the money and time problems. Even, testing should be performed at every step in the software development life cycle (SDLC) which is a structured approach used in the development of the software product. Software testing is a tradeoff between budget, time and quality. Now a day, testing becomes a very important activity in terms of exposure, security, performance and usability. Hence, software testing faces a collection of challenges.
Top 7 reasons why software testing is crucial in SDLCSLAJobs Chennai
Software Testing is an important thing in Software Development Life Cycle (SDLC) because errors can occur anywhere in the application and some of them remain undiscovered until deployment. The final product with bugs brings many difficulties such as redevelopment, wastage of cost and time, poor functionality, and many more. Software testing proves the quality of the product that helps to gain more customer satisfaction by meeting their requirements in an application or a product.
FROM THE ART OF SOFTWARE TESTING TO TEST-AS-A-SERVICE IN CLOUD COMPUTINGijseajournal
Researchers consider that the first edition of the book "The Art of Software Testing" by Myers (1979)
initiated research in Software Testing. Since then, software testing has gone through evolutions that have
driven standards and tools. This evolution has accompanied the complexity and variety of software
deployment platforms. The migration to the cloud allowed benefits such as scalability, agility, and better
return on investment. Cloud computing requires more significant involvement in software testing to ensure
that services work as expected. In addition to testing cloud applications, cloud computing has paved the
way for testing in the Test-as-a-Service model. This review aims to understand software testing in the
context of cloud computing. Based on the knowledge explained here, we sought to linearize the evolution of
software testing, characterizing fundamental points and allowing us to compose a synthesis of the body of
knowledge in software testing, expanded by the cloud computing paradigm.
From the Art of Software Testing to Test-as-a-Service in Cloud Computingijseajournal
Researchers consider that the first edition of the book "The Art of Software Testing" by Myers (1979)
initiated research in Software Testing. Since then, software testing has gone through evolutions that have
driven standards and tools. This evolution has accompanied the complexity and variety of software
deployment platforms. The migration to the cloud allowed benefits such as scalability, agility, and better
return on investment. Cloud computing requires more significant involvement in software testing to ensure
that services work as expected. In addition to testing cloud applications, cloud computing has paved the
way for testing in the Test-as-a-Service model. This review aims to understand software testing in the
context of cloud computing. Based on the knowledge explained here, we sought to linearize the evolution of
software testing, characterizing fundamental points and allowing us to compose a synthesis of the body of
knowledge in software testing, expanded by the cloud computing paradigm.
Why Software Testing is Crucial in Software Development_.pdfXDuce Corporation
Software testing is the process of verifying and then confirming that a software application or product
performs as expected or not. Testing has its own set of advantages like bug prevention, lower costs of
development, and comparatively better performance.
The software cannot be said to be bug-free from the start. Therefore, software developers might strive
to write code that will reduce the number and severity of flaws that are already there. However, the
majority of bugs are latent and only emerge when the conditions are right.
12 Different Software Testing Methodologies.pdfOprim Solutions
Software testing plays a crucial role in ensuring the quality, reliability, and functionality of software products. With numerous methodologies available, it can be challenging to determine which approach is best suited for your project. Here we will explore 12 different software testing methodologies, providing you with a comprehensive overview of each. For more visit us at :- https://oprim.ca/ or Contact us at+1(833)556-7746.
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Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
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Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
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• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
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SRGM Analyzers Tool of SDLC for Software Improving Quality
1. Mr. Girish Nille Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 11(Version - 5), November 2014, pp.47-50
www.ijera.com 47 | P a g e
SRGM Analyzers Tool of SDLC for Software Improving Quality Mr. Girish Nille, Prof. Bharat Tidake
Abstract— Software Reliability Growth Models (SRGM) have been developed to estimate software reliability measures such as software failure rate, number of remaining faults and software reliability. In this paper, the software analyzers tool proposed for deriving several software reliability growth models based on Enhanced Non-homogeneous Poisson Process (ENHPP) in the presence of imperfect debugging and error generation. The proposed models are initially formulated for the case when there is no differentiation between failure observation and fault removal testing processes and then this extended for the case when there is a clear differentiation between failure observation and fault removal testing processes. Many Software Reliability Growth Models (SRGM) have been developed to describe software failures as a random process and can be used to measure the development status during testing. With SRGM software consultants can easily measure (or evaluate) the software reliability (or quality) and plot software reliability growth charts. Keywords— Software Reliability Growth Model (SRGM), Testing Coverage (TC), Testing Time, Cobb Douglas Production Function, ENHPP (Enhanced Non-Homogenous Poisson Process), Fault Detection Rate (FDR).
I. INTRODUCTION The concern for software reliability has grown over a period of time especially with the advent of real life systems such as satellite and shuttle control, telephone, internet and banking services. With the growing economy there has been a growing interest of companies to know about their competitors and have been spending a lot on strategic decision making. All these activities require complex software systems. It is important that these systems are thoroughly tested before implementation. There is a huge cost attached with fixing failures, safety concerns, and legal liabilities therefore organizations need to produce software that is reliable. There are several methodologies to develop software but questions that need to be addressed are how many times will the software fail and when, how to estimate testing coverage, when to stop testing, and when to release the software. Also, for a software product there is need to predict/estimate the maintenance effort; for example, how long the warranty period must be, once the software is released, how many defects can be expected at what severity levels, how many engineers are required to support the product, for how long, and so forth[1].
Software reliability assessment is an important issue for planning release of high-quality software products to users. Many developers have proposed software reliability growth models (SRGMs) to assess software quantitatively from fault data observed in software testing phase [1-2, 3-4]. Software reliability is defined as the probability of failure free software operation for a specified period of time in a specified environment. It is used to assess the reliability of the software during testing and operational phases. Software testing involves running the software and checking for unexpected behavior in software output. The successful test can be considered to be one, which reveals the presence of the latent faults. The process of locating the faults and designing the procedures to detect them was called the debugging process. The chronology of failure occurrence and fault detections can be utilized to provide an estimate of the software reliability and the level of fault content. In particular, the Enhanced non- homogeneous Poisson process (ENHPP) [10] based SRGMs are quite popular due to their mathematical tractability, and there have been a number of ENHPP- based SRGMs proposed by many Developers. In spite of the diversity and elegance of many of these, no single model can be readily recommended as best to represent the challenging nature of the software testing[9,7].
As reliability is of great concern for software products this field is catching attention of various researchers and practitioners. This has lead to a new concept Software Reliability Growth Modeling. An SRGM is defined as a tool that can be used to evaluate the software quantitatively, develop test status, schedule status, and monitor the changes in reliability performance. Software reliability assessment and prediction is important to evaluate the performance of software system. The reliability of the software is quantified in terms of the estimated number of faults remaining in the software system. During the testing phase, the emphasis is on reducing the remaining fault content hence increasing the software reliability. The SRGMs developed in literature are either dependent on testing time, testing effort or testing coverage. Testing time is the calendar time or the CPU time whereas testing effort takes into account the manpower time and the CPU time. The papers discuss some novel software reliability growth models dependent on time. Testing Coverage plays a very important role in predicting the software reliability. Testing Coverage is actually a structural testing technique in which the software performance is judged with respect to specification of the source
RESEARCH ARTICLE OPEN ACCESS
2. Mr. Girish Nille Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 11(Version - 5), November 2014, pp.47-50
www.ijera.com 48 | P a g e
code and the extent or the degree to which software is executed by the test cases. TC can help software developers to evaluate the quality of the tested software and determine how much additional effort is needed to improve the reliability of the software besides providing customers with a quantitative confidence criterion while planning to use a software product. Hence, safety critical system has a high coverage objective. II. LITERATURE SURVEY Various investigative programming dependability shows have been proposed for evaluating the unwavering quality development of a programming item. The paper present an Enhanced non- homogeneous Poisson process (ENHPP) model [10] and show that awhile ago reported Non-homogeneous Poisson process (NHPP) based models, with limited mean esteem capacities, are extraordinary instances of the (ENHPP) model. The (ENHPP) model contrasts from past models in that it consolidates unequivocally the time differing test scope capacity in its analytical definition, and accommodates damaged blame discovery in the testing stage and test scope throughout the testing and operational stages . The (ENHPP) model is validated utilizing a few accessible inadequacy information sets. Many Developers have incorporated change point in software reliability growth modeling. Firstly Zhao [5] incorporated change-point in software and hardware reliability. Huang et al. [4] used change- point in software reliability growth modeling with testing effort functions. The imperfect debugging with change-point has been introduced in software reliability growth modeling by Shyur. Kapur et al. [6, 8] introduced various testing effort functions and testing effort control with change-point in software reliability growth modeling. The multiple change- points in software reliability growth modeling for fielded software have been proposed by Kapur et al. [6, 7]. A testing coverage based SRGM was proposed by Malaiya [6]. Inoue and Yamada [5] developed SRGM to describe a time-dependent behavior of a testing-coverage attainment process with the testing- skill of test-case designer. III. STATEMENT OF AIM AND OBJECTIVE Aim of this model is achieve the reliability of the software and Increases role of software in real life systems. Objectives are to manage and improve:
The reliability of the software
Check the efficiency of development activities
Evaluate the software reliability at the end of validation activities and in operation
Estimate the maintenance effort to “correct” faults activated during development and residual faults in operation.
IV. PROPOSED SYSTEM MODEL The paper has propose a generalized framework for deriving several testing time and testing coverage depends on Enhanced Non-Homogeneous Poisson Process software reliability growth model which incorporate Change point. Change point is one of the interesting phenomenons on observed during software development. Inclusion of change point in software reliability growth modeling enhances the predictive accuracy of the model. The proposed models are based upon the following basic assumptions.
The failure observation and fault removal phenomenon is modeled using an ENHPP [10].
Software is subject to failures during execution caused by faults remaining in the software.
Each time a failure is observed, an immediate debugging effort takes place to find the cause of the failure to remove it.
The failure rate is equally affected by all the faults remaining in the software.
When a software failure occurs, an instantaneous repair effort starts, and then either (a) the fault content is reduced by one with probability , or (b) the fault content remains unchanged with probability .
During the fault removal process, whether the fault is removed successfully or not, new faults are generated with a constant probability.
Fig. 1 ENHPP Model A] ENHPP Model Phases i] Phases of Analyzer: 1. Identifying Phase [11] This phase consist identifying software such as TNOA, WMPC, NOCP etc. as shown below. TNOC: It counts total of attributes for a class. WMPC: It counts all class methods per class.
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ISSN : 2248-9622, Vol. 4, Issue 11(Version - 5), November 2014, pp.47-50
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NOCP: It Count the Number of classes in a package. MIT: It calculates the longest path from the class to the root of the inheritance tree. CCIM: It measures the number of linearly independent paths through a program module .i.e. the amount of decision logic in a single software factor. It is calculated per class. SIZE: It refers to the count total lines of code. 2. Evaluation Phase [11] This phase consists of evaluating the various software measure written as a part of “Analyzer” tool development which are provided for examination 3. Interpretation and Advising Phase [11] The interpretation part of the measure Analyzer takes the extracted measure values in measure Evaluation Phase from the source code and compares these values with the threshold values of the corresponding metric values. If the extracted metric values lies below the corresponding metric threshold value means that no occurrence of the issue that is being observed and extracted measure values lies above the corresponding metric threshold value means maximum occurrence of the observed issue. 4. Application Phase [11] In this phase, software refactoring is done .Our tool “measure Analyzer” applies the Object Oriented metrics on the code base and these metric values are then interpreted. Then various refactoring techniques were used to improve the code design and along with that paper has also studied the impact of refactoring on the software quality through various measures. V. METHODOLOGIES AND TECHNIQUES ENHPP (Enhanced Non-Homogeneous Poisson Process) model – [10] The ENHPP model provides a unifying framework for finite failure software reliability growth models According to this model, the expected number of faults detected by time t, called the mean value function, n (t) is of the form: n (t) = b * c (t)
Where b is the needed number of shortcomings in the programming (before testing/debugging starts), and c (t) is the scope capacity. The ENHPP model utilized by SREPT gives by default to reflect four sorts of inadequacy event rates for every fault. Inter flop times information acquired from the testing stage could be utilized to parameterize the ENHPP (Enhanced Non Homogeneous Poisson Process) model to acquire gauges of the inadequacy force, number of flaws remaining, dependability after discharge, and scope for the programming. When complexity metrics are available, the total number of faults in the software can be estimated using the fault density approach or the regression tree model. If the number of lines of code in the software is NL, the expected number of faults can be estimated as, [4]:
F = NL * FD The relapse tree model is an objective arranged statistical method, which endeavors to foresee the amount of deficiencies in a programming module dependent upon the static unpredictability measurements. The structure of the ENHPP model might additionally be utilized to join the appraisal of the aggregate number of issues acquired after the testing stage dependent upon unpredictability measurements (parameter a), with the scope informative content got throughout the testing stage (c (t)). The ENHPP model [10] can also use inter failure times from the testing phase to obtain release times (optimal time to stop testing) for the software on the basis of a specified release criteria. Release criteria [10] could be of the following types – Number of remaining faults - In this case, the release time is when a fraction of all detectable faults has been removed. Failure intensity requirements - The criterion based on failure intensity suggests that the software should be released when the failure intensity measured at the end of the development test phase reaches a specified value f. Reliability requirements - This criteria could be used to specify that the required conditional reliability in the operational phase is, say Rr at time t0 after product release. Cost requirements - From a knowledge of the expected cost of removing a fault during testing, the expected cost of removing a fault during operation, and the expected cost of software testing per unit time, the total cost can be estimated. The release time is obtained by determining the time that minimizes this total cost. Availability requirements - The release time can be estimated based on an operational availability requirement [2]. The Cobb-Douglas [10] employs production function to demonstrate the effect of both testing time and testing coverage in removing the faults in the software. The SRGMs developed in literature are either dependent on testing time, testing effort or testing coverage. Testing time is the calendar time or the CPU time whereas testing effort takes into account the manpower time and the CPU time. The mathematical form of the production function is follows: Where:
Y = total production (the monetary value of all goods produced in a year)