Estimation of resources, cost, and schedule for a software engineering effort requires experience, access to good historical information, and the courage to commit to quantitative predictions when qualitative information is all that exists. Halstead’s Measure & COCOMO Modeol COCOMO II Model of Estimation techniquesused or S/w Developments and Maintenance
Effort estimation for software developmentSpyros Ktenas
Software effort estimation has been an important issue for almost everyone in software industry at some point. Below I will try to give some basic details on methods, best practices, common mistakes and available tools.
You may also check a tool implementing methods for estimation at http://effort-estimation.gatory.com/
Spyros Ktenas
http://open-works.org/profiles/spyros-ktenas
AN APPROACH FOR SOFTWARE EFFORT ESTIMATION USING FUZZY NUMBERS AND GENETIC AL...csandit
One of the most critical tasks during the software development life cycle is that of estimating the effort and time involved in the development of the software product. Estimation may be performed by many ways such as: Expert judgments, Algorithmic effort estimation, Machine
learning and Analogy-based estimation. In which Analogy-based software effort estimation is the process of identifying one or more historical projects that are similar to the project being developed and then using the estimates from them. Analogy-based estimation is integrated with Fuzzy numbers in order to improve the performance of software project effort estimation during
the early stages of a software development lifecycle. Because of uncertainty associated with attribute measurement and data availability, fuzzy logic is introduced in the proposed model.But hardly a historical project is exactly same as the project being estimated due to some distance associated in similarity distance. This means that the most similar project still has a
similarity distance with the project being estimated in most of the cases. Therefore, the effort needs to be adjusted when the most similar project has a similarity distance with the project being estimated. To adjust the reused effort, we build an adjustment mechanism whose
algorithm can derive the optimal adjustment on the reused effort using Genetic Algorithm. The proposed model Combine the fuzzy logic to estimate software effort in early stages with Genetic algorithm based adjustment mechanism may result to near the correct effort estimation.
An approach for software effort estimation using fuzzy numbers and genetic al...csandit
One of the most critical tasks during the software development life cycle is that of estimating the
effort and time involved in the development of the software product. Estimation may be
performed by many ways such as: Expert judgments, Algorithmic effort estimation, Machine
learning and Analogy-based estimation. In which Analogy-based software effort estimation is
the process of identifying one or more historical projects that are similar to the project being
developed and then using the estimates from them. Analogy-based estimation is integrated with
Fuzzy numbers in order to improve the performance of software project effort estimation during
the early stages of a software development lifecycle. Because of uncertainty associated with
attribute measurement and data availability, fuzzy logic is introduced in the proposed model.
But hardly a historical project is exactly same as the project being estimated due to some
distance associated in similarity distance. This means that the most similar project still has a
similarity distance with the project being estimated in most of the cases. Therefore, the effort
needs to be adjusted when the most similar project has a similarity distance with the project
being estimated. To adjust the reused effort, we build an adjustment mechanism whose
algorithm can derive the optimal adjustment on the reused effort using Genetic Algorithm. The
proposed model Combine the fuzzy logic to estimate software effort in early stages with Genetic
algorithm based adjustment mechanism may result to near the correct effort estimation.
Effort estimation is the process of predicting the most realistic amount of effort (expressed in terms of person-hours or money) required to develop or maintain software based on incomplete, uncertain and noisy input.
Effort estimation is essential for many people and different departments in an organization.
call for papers, research paper publishing, where to publish research paper, journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJEI, call for papers 2012,journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, research and review articles, engineering journal, International Journal of Engineering Inventions, hard copy of journal, hard copy of certificates, journal of engineering, online Submission, where to publish research paper, journal publishing, international journal, publishing a paper, hard copy journal, engineering journal
Effort estimation for software developmentSpyros Ktenas
Software effort estimation has been an important issue for almost everyone in software industry at some point. Below I will try to give some basic details on methods, best practices, common mistakes and available tools.
You may also check a tool implementing methods for estimation at http://effort-estimation.gatory.com/
Spyros Ktenas
http://open-works.org/profiles/spyros-ktenas
AN APPROACH FOR SOFTWARE EFFORT ESTIMATION USING FUZZY NUMBERS AND GENETIC AL...csandit
One of the most critical tasks during the software development life cycle is that of estimating the effort and time involved in the development of the software product. Estimation may be performed by many ways such as: Expert judgments, Algorithmic effort estimation, Machine
learning and Analogy-based estimation. In which Analogy-based software effort estimation is the process of identifying one or more historical projects that are similar to the project being developed and then using the estimates from them. Analogy-based estimation is integrated with Fuzzy numbers in order to improve the performance of software project effort estimation during
the early stages of a software development lifecycle. Because of uncertainty associated with attribute measurement and data availability, fuzzy logic is introduced in the proposed model.But hardly a historical project is exactly same as the project being estimated due to some distance associated in similarity distance. This means that the most similar project still has a
similarity distance with the project being estimated in most of the cases. Therefore, the effort needs to be adjusted when the most similar project has a similarity distance with the project being estimated. To adjust the reused effort, we build an adjustment mechanism whose
algorithm can derive the optimal adjustment on the reused effort using Genetic Algorithm. The proposed model Combine the fuzzy logic to estimate software effort in early stages with Genetic algorithm based adjustment mechanism may result to near the correct effort estimation.
An approach for software effort estimation using fuzzy numbers and genetic al...csandit
One of the most critical tasks during the software development life cycle is that of estimating the
effort and time involved in the development of the software product. Estimation may be
performed by many ways such as: Expert judgments, Algorithmic effort estimation, Machine
learning and Analogy-based estimation. In which Analogy-based software effort estimation is
the process of identifying one or more historical projects that are similar to the project being
developed and then using the estimates from them. Analogy-based estimation is integrated with
Fuzzy numbers in order to improve the performance of software project effort estimation during
the early stages of a software development lifecycle. Because of uncertainty associated with
attribute measurement and data availability, fuzzy logic is introduced in the proposed model.
But hardly a historical project is exactly same as the project being estimated due to some
distance associated in similarity distance. This means that the most similar project still has a
similarity distance with the project being estimated in most of the cases. Therefore, the effort
needs to be adjusted when the most similar project has a similarity distance with the project
being estimated. To adjust the reused effort, we build an adjustment mechanism whose
algorithm can derive the optimal adjustment on the reused effort using Genetic Algorithm. The
proposed model Combine the fuzzy logic to estimate software effort in early stages with Genetic
algorithm based adjustment mechanism may result to near the correct effort estimation.
Effort estimation is the process of predicting the most realistic amount of effort (expressed in terms of person-hours or money) required to develop or maintain software based on incomplete, uncertain and noisy input.
Effort estimation is essential for many people and different departments in an organization.
call for papers, research paper publishing, where to publish research paper, journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJEI, call for papers 2012,journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, research and review articles, engineering journal, International Journal of Engineering Inventions, hard copy of journal, hard copy of certificates, journal of engineering, online Submission, where to publish research paper, journal publishing, international journal, publishing a paper, hard copy journal, engineering journal
Function Point Software Cost Estimates using Neuro-Fuzzy techniqueijceronline
Software estimation accuracy is among the greatest challenges for software developers. As Neurofuzzy based system is able to approximate the non-linear function with more precision so it is used as a soft computing approach to generate model by formulating the relationship based on its training. The approach presented in this paper is independent of the nature and type of estimation. In this paper, Function point is used as algorithmic model and an attempt is being made to validate the soundness of Neuro fuzzy technique using ISBSG and NASA project data.
SOFTWARE COST ESTIMATION USING FUZZY NUMBER AND PARTICLE SWARM OPTIMIZATIONIJCI JOURNAL
Software cost estimation is a process to calculate effort, time and cost of a project, and assist in better
decision making about the feasibility or viability of project. Accurate cost prediction is required to
effectively organize the project development tasks and to make economical and strategic planning, project
management. There are several known and unknown factors affect this process, so cost estimation is a very
difficult process. Software size is a very important factor that impacts the process of cost estimation.
Accuracy of cost estimation is directly proportional to the accuracy of the size estimation.
Failure of Software projects has always been an important area of focus for the Software Industry.
Implementation phase is not the only phase for Software projects to fail, instead planning and estimation
steps are the most crucial ones, which lead to their failure. More than 50% of the total projects fail which
go beyond the estimated time and cost. The Standish group‘s CHAOS reports failure rate of 70% for the
software projects. This paper presents the existing algorithms for software estimation and the relevant
concepts of Fuzzy Theory and PSO. Also explains the proposed algorithm with experimental results.
Insights on Research Techniques towards Cost Estimation in Software Design IJECEIAES
Software cost estimation is of the most challenging task in project management in order to ensuring smoother development operation and target achievement. There has been evolution of various standards tools and techniques for cost estimation practiced in the industry at present times. However, it was never investigated about the overall picturization of effectiveness of such techniques till date. This paper initiates its contribution by presenting taxonomies of conventional cost-estimation techniques and then investigates the research trends towards frequently addressed problems in it. The paper also reviews the existing techniques in well-structured manner in order to highlight the problems addressed, techniques used, advantages associated and limitation explored from literatures. Finally, we also brief the explored open research issues as an added contribution to this manuscript.
The presentation was prepared and delivered to fulfill the curriculum requirement of PROTON business school, Indore in 2nd trimester of IT & System group.
In the present paper, applicability and
capability of A.I techniques for effort estimation prediction has
been investigated. It is seen that neuro fuzzy models are very
robust, characterized by fast computation, capable of handling
the distorted data. Due to the presence of data non-linearity, it is
an efficient quantitative tool to predict effort estimation. The one
hidden layer network has been developed named as OHLANFIS
using MATLAB simulation environment.
Here the initial parameters of the OHLANFIS are
identified using the subtractive clustering method. Parameters of
the Gaussian membership function are optimally determined
using the hybrid learning algorithm. From the analysis it is seen
that the Effort Estimation prediction model developed using
OHLANFIS technique has been able to perform well over normal
ANFIS Model.
EARLY STAGE SOFTWARE DEVELOPMENT EFFORT ESTIMATIONS – MAMDANI FIS VS NEURAL N...cscpconf
Accurately estimating the software size, cost, effort and schedule is probably the biggest
challenge facing software developers today. It has major implications for the management of
software development because both the overestimates and underestimates have direct impact for
causing damage to software companies. Lot of models have been proposed over the years by
various researchers for carrying out effort estimations. Also some of the studies for early stage
effort estimations suggest the importance of early estimations. New paradigms offer alternatives
to estimate the software development effort, in particular the Computational Intelligence (CI)
that exploits mechanisms of interaction between humans and processes domain
knowledge with the intention of building intelligent systems (IS). Among IS,
Artificial Neural Network and Fuzzy Logic are the two most popular soft computing techniques
for software development effort estimation. In this paper neural network models and Mamdani
FIS model have been used to predict the early stage effort estimations using the student dataset.
It has been found that Mamdani FIS was able to predict the early stage efforts more efficiently in
comparison to the neural network models based models.
COMPARATIVE STUDY OF SOFTWARE ESTIMATION TECHNIQUES ijseajournal
Many information technology firms among other organizations have been working on how to perform estimation of the sources such as fund and other resources during software development processes. Software development life cycles require lot of activities and skills to avoid risks and the best software estimation technique is supposed to be employed. Therefore, in this research, a comparative study was conducted, that consider the accuracy, usage, and suitability of existing methods. It will be suitable for the project managers and project consultants during the whole software project development process. In this project technique such as linear regression; both algorithmic and non-algorithmic are applied. Model, composite and regression techniques are used to derive COCOMO, COCOMO II, SLIM and linear multiple respectively. Moreover, expertise-based and linear-based rules are applied in non-algorithm methods. However, the technique needs some advancement to reduce the errors that are experienced during the software development process. Therefore, this paper in relation to software estimation techniques has proposed a model that can be helpful to the information technology firms, researchers and other firms that use information technology in the processes such as budgeting and decision-making processes.
New approach for solving software project scheduling problem using differenti...ijfcstjournal
Software Project Scheduling Problem (SPSP) is one of the most critical issues in developing software. The
major factor in completing the software project consistent with planned cost and schedule is implementing
accurate and true scheduling. The subject of SPSP is an important topic which in software projects
development and management should be considered over other topics and software project development
must be done on it. SPSP usually includes resources planning, cost estimates, staffing and cost control.
Therefore, it is necessary for SPSP use an algorithmic that with considering of costs and limited resources
can estimate optimal time for completion of the project. Simultaneously reduce of time and cost in software
projects development is very vital and necessary. The meta-heuristic algorithms has good performance in
SPSP in recent years. When software projects factors are vague and incomplete, cost based scheduling
models based on meta-heuristic algorithms can look better at scheduling. This algorithm works based on
the Collective Intelligence and using the fitness function, it has more accurate ability for SPSP. In this
paper, Differential Evolution (DE) algorithm is used to optimize SPSP. Experimental results show that the
proposed model has better performance than Genetic Algorithm (GA).
Review on cost estimation technque for web application [part 1]Sayed Mohsin Reza
Prepared by Sayed Mohsin Reza
Part 1
Source Book: Cost Estimation Techniques for Web Projects
BOOK AUTHOR: EMILIA MENDES UNIVERSITY OF AUCKLAND, NEW ZEALAND
Difference about Web and Software Projects
Introduction to Web Cost Estimation
Accuracy of an Effort Model?
Sizing Web Applications
The International Journal of Engineering and Science (IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Function Point Software Cost Estimates using Neuro-Fuzzy techniqueijceronline
Software estimation accuracy is among the greatest challenges for software developers. As Neurofuzzy based system is able to approximate the non-linear function with more precision so it is used as a soft computing approach to generate model by formulating the relationship based on its training. The approach presented in this paper is independent of the nature and type of estimation. In this paper, Function point is used as algorithmic model and an attempt is being made to validate the soundness of Neuro fuzzy technique using ISBSG and NASA project data.
SOFTWARE COST ESTIMATION USING FUZZY NUMBER AND PARTICLE SWARM OPTIMIZATIONIJCI JOURNAL
Software cost estimation is a process to calculate effort, time and cost of a project, and assist in better
decision making about the feasibility or viability of project. Accurate cost prediction is required to
effectively organize the project development tasks and to make economical and strategic planning, project
management. There are several known and unknown factors affect this process, so cost estimation is a very
difficult process. Software size is a very important factor that impacts the process of cost estimation.
Accuracy of cost estimation is directly proportional to the accuracy of the size estimation.
Failure of Software projects has always been an important area of focus for the Software Industry.
Implementation phase is not the only phase for Software projects to fail, instead planning and estimation
steps are the most crucial ones, which lead to their failure. More than 50% of the total projects fail which
go beyond the estimated time and cost. The Standish group‘s CHAOS reports failure rate of 70% for the
software projects. This paper presents the existing algorithms for software estimation and the relevant
concepts of Fuzzy Theory and PSO. Also explains the proposed algorithm with experimental results.
Insights on Research Techniques towards Cost Estimation in Software Design IJECEIAES
Software cost estimation is of the most challenging task in project management in order to ensuring smoother development operation and target achievement. There has been evolution of various standards tools and techniques for cost estimation practiced in the industry at present times. However, it was never investigated about the overall picturization of effectiveness of such techniques till date. This paper initiates its contribution by presenting taxonomies of conventional cost-estimation techniques and then investigates the research trends towards frequently addressed problems in it. The paper also reviews the existing techniques in well-structured manner in order to highlight the problems addressed, techniques used, advantages associated and limitation explored from literatures. Finally, we also brief the explored open research issues as an added contribution to this manuscript.
The presentation was prepared and delivered to fulfill the curriculum requirement of PROTON business school, Indore in 2nd trimester of IT & System group.
In the present paper, applicability and
capability of A.I techniques for effort estimation prediction has
been investigated. It is seen that neuro fuzzy models are very
robust, characterized by fast computation, capable of handling
the distorted data. Due to the presence of data non-linearity, it is
an efficient quantitative tool to predict effort estimation. The one
hidden layer network has been developed named as OHLANFIS
using MATLAB simulation environment.
Here the initial parameters of the OHLANFIS are
identified using the subtractive clustering method. Parameters of
the Gaussian membership function are optimally determined
using the hybrid learning algorithm. From the analysis it is seen
that the Effort Estimation prediction model developed using
OHLANFIS technique has been able to perform well over normal
ANFIS Model.
EARLY STAGE SOFTWARE DEVELOPMENT EFFORT ESTIMATIONS – MAMDANI FIS VS NEURAL N...cscpconf
Accurately estimating the software size, cost, effort and schedule is probably the biggest
challenge facing software developers today. It has major implications for the management of
software development because both the overestimates and underestimates have direct impact for
causing damage to software companies. Lot of models have been proposed over the years by
various researchers for carrying out effort estimations. Also some of the studies for early stage
effort estimations suggest the importance of early estimations. New paradigms offer alternatives
to estimate the software development effort, in particular the Computational Intelligence (CI)
that exploits mechanisms of interaction between humans and processes domain
knowledge with the intention of building intelligent systems (IS). Among IS,
Artificial Neural Network and Fuzzy Logic are the two most popular soft computing techniques
for software development effort estimation. In this paper neural network models and Mamdani
FIS model have been used to predict the early stage effort estimations using the student dataset.
It has been found that Mamdani FIS was able to predict the early stage efforts more efficiently in
comparison to the neural network models based models.
COMPARATIVE STUDY OF SOFTWARE ESTIMATION TECHNIQUES ijseajournal
Many information technology firms among other organizations have been working on how to perform estimation of the sources such as fund and other resources during software development processes. Software development life cycles require lot of activities and skills to avoid risks and the best software estimation technique is supposed to be employed. Therefore, in this research, a comparative study was conducted, that consider the accuracy, usage, and suitability of existing methods. It will be suitable for the project managers and project consultants during the whole software project development process. In this project technique such as linear regression; both algorithmic and non-algorithmic are applied. Model, composite and regression techniques are used to derive COCOMO, COCOMO II, SLIM and linear multiple respectively. Moreover, expertise-based and linear-based rules are applied in non-algorithm methods. However, the technique needs some advancement to reduce the errors that are experienced during the software development process. Therefore, this paper in relation to software estimation techniques has proposed a model that can be helpful to the information technology firms, researchers and other firms that use information technology in the processes such as budgeting and decision-making processes.
New approach for solving software project scheduling problem using differenti...ijfcstjournal
Software Project Scheduling Problem (SPSP) is one of the most critical issues in developing software. The
major factor in completing the software project consistent with planned cost and schedule is implementing
accurate and true scheduling. The subject of SPSP is an important topic which in software projects
development and management should be considered over other topics and software project development
must be done on it. SPSP usually includes resources planning, cost estimates, staffing and cost control.
Therefore, it is necessary for SPSP use an algorithmic that with considering of costs and limited resources
can estimate optimal time for completion of the project. Simultaneously reduce of time and cost in software
projects development is very vital and necessary. The meta-heuristic algorithms has good performance in
SPSP in recent years. When software projects factors are vague and incomplete, cost based scheduling
models based on meta-heuristic algorithms can look better at scheduling. This algorithm works based on
the Collective Intelligence and using the fitness function, it has more accurate ability for SPSP. In this
paper, Differential Evolution (DE) algorithm is used to optimize SPSP. Experimental results show that the
proposed model has better performance than Genetic Algorithm (GA).
Review on cost estimation technque for web application [part 1]Sayed Mohsin Reza
Prepared by Sayed Mohsin Reza
Part 1
Source Book: Cost Estimation Techniques for Web Projects
BOOK AUTHOR: EMILIA MENDES UNIVERSITY OF AUCKLAND, NEW ZEALAND
Difference about Web and Software Projects
Introduction to Web Cost Estimation
Accuracy of an Effort Model?
Sizing Web Applications
The International Journal of Engineering and Science (IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
AN APPROACH FOR SOFTWARE EFFORT ESTIMATION USING FUZZY NUMBERS AND GENETIC AL...cscpconf
One of the most critical tasks during the software development life cycle is that of estimating the effort and time involved in the development of the software product. Estimation may be performed by many ways such as: Expert judgments, lgorithmic effort estimation, Machine learning and Analogy-based estimation. In which Analogy-based software effort estimation is
the process of identifying one or more historical projects that are similar to the project being developed and then using the estimates from them. Analogy-based estimation is integrated with Fuzzy numbers in order to improve the performance of software project effort estimation during the early stages of a software development lifecycle. Because of uncertainty associated with tribute measurement and data availability, fuzzy logic is introduced in the proposed model. But hardly a historical project is exactly same as the project being estimated due to some distance associated in similarity distance. This means that the most similar project still has a similarity distance with the project being estimated in most of the cases. Therefore, the effort needs to be adjusted when the most similar project has a similarity distance with the project being estimated. To adjust the reused effort, we build an adjustment mechanism whose
algorithm can derive the optimal adjustment on the reused effort using Genetic Algorithm. The proposed model Combine the fuzzy logic to estimate software effort in early stages with Genetic algorithm based adjustment mechanism may result to near the correct effort estimation.
How Should We Estimate Agile Software Development Projects and What Data Do W...Glen Alleman
Estimating techniques for an acquisition program progresses from analogies to actual cost method as the program matures and more information is known. The analogy method is most appropriate early in the program life cycle when the system is not yet fully defined.
The peer-reviewed International Journal of Engineering Inventions (IJEI) is started with a mission to encourage contribution to research in Science and Technology. Encourage and motivate researchers in challenging areas of Sciences and Technology.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Abstract The management of software cost, development effort and project planning are the key aspects of software development. Throughout the sixty-odd years of software development, the industry has gone at least four generations of programming languages and three major development paradigms. Still the total ability to move consistently from idea to product is yet to be achieved. In fact, recent studies document that the failure rate for software development has risen almost to 50 percent. There is no magic in managing software development successfully, but a number of issues related to software development make it unique. The basic problem of software development is risky. Some example of risk is error in estimation, schedule slips, project cancelled after numerous slips, high defect rate, system goes sour, business misunderstanding, false feature rich, staff turnover. XSoft Estimation addresses the risks by accurate measurement. A new methodology to estimate using software COSMIC-Full Function Point and named as EXtreme Software Estimation (XSoft Estimation). Based on the experience gained on the original XSoft project develpment, this paper describes what makes XSoft Estimation work from sizing to estimation. Keywords: -COSMIC function size unit, XSoft Estimation, XSoft Measurement, Cost Estimation.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
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2.
The software project planner must
estimate three things before a project
begins:
how long it will take
how much effort will be required
how many people will be involved
Software Project
Planner
3.
Estimation of resources, cost, and
schedule for a software engineering effort
requires experience, access to good
historical information, and the courage to
commit to quantitative predictions when
qualitative information is all that exists.
Project Estimation
6.
By comparing and reconciling
estimates derived using different
techniques the planner is more
likely to derive an accurate
estimate.
Estimation
7.
Software cost and effort
estimation will never be an exact
science, but a combination of
good historical data and
systematic techniques can
improve estimation accuracy.
Estimation
8.
To achieve reliable cost and effort estimates, a number
of options arise:
1. Delay estimation until late in the project (obviously,
we can achieve 100% accurate estimates after the project
is complete!).
2. Base estimates on similar projects that have already
been completed.
3. Use relatively simple decomposition techniques to
generate project cost and effort estimates.
4. Use one or more empirical models for software cost
and effort estimation.
Reliable Cost and Effort
Estimates
9.
These automated estimation tools
allow the planner to estimate cost
and effort and to perform "what-if"
analyses for important project
variables such as delivery date or
staffing.
Automated Estimation
Tools
11.
The “size” of one or more software work
products is estimated. Work products
include the external representation of
software (e.g., screen, reports), the
software itself (e.g., KLOC), functionality
delivered (e.g., function points),
descriptive information (e.g. documents).
Sizing of project
deliverables
12.
The appropriate process framework
is selected and the software
engineering task set is specified.
Selecting project activities
13.
The number of people who will be
available to do the work is specified.
Because the relationship between people
available and work (predicted effort) is
highly nonlinear, this is an important
input.
Predicting staffing levels
14.
Estimation tools use one or more
models that relate the size of the
project deliverables to the effort
required to produce them.
Predicting software effort
15.
Given the results of Predicting
software effort, costs can be
computed by allocating labor rates to
the project activities noted in
Selecting project activities.
Predicting software cost
16.
When effort, staffing level, and project
activities are known, a draft schedule can
be produced by allocating labor across
software engineering activities based on
recommended models for effort
distribution.
Predicting software
schedules
18.
Decomposition techniques require a delineation of
major software functions, followed by estimates of
either
(1) the number of LOC
(2) selected values within the information domain
(3) the number of person-months required to
implement each function
(4) the number of person-months required for each
software engineering activity.
Decomposition
Techniques
19.
Empirical techniques use empirically
derived expressions for effort and
time to predict these project
quantities.
Automated tools can be used to
implement a specific empirical
model.
Empirical Techniques
20.
The accuracy of a software project estimate is
predicated on
(1)the degree to which the planner has properly
estimated the size of the product to be built.
(2) the ability to translate the size estimate into human
effort, calendar time, and dollars (a function of the
availability of reliable software metrics from past
projects).
(3) the degree to which the project plan reflects the
abilities of the software team.
(4) the stability of product requirements and the
environment that supports the software engineering
effort.
Software Sizing
22.
This approach uses the approximate reasoning
techniques that are the cornerstone of fuzzy
logic. To apply this approach, the planner must
identify the type of application, establish its
magnitude on a qualitative scale, and then
refine the magnitude within the original range.
Although personal experience can be used, the
planner should also have access to a historical
database of projects so that estimates can be
compared to actual experience.
Fuzzy Logic Sizing
23.
The planner develops estimates of
the information domain
characteristics.
Function Point Sizing
24. Software is composed of a number of different
“standard components” that are generic to a particular
application area.
For example, the standard components for an information
system are subsystems, modules, screens, reports, interactive
programs, batch programs, files, LOC, and object-level
instructions. The planner estimates that 18 reports will be
generated. Historical data indicates that 967 lines of
COBOL[PUT92] are required per report. This enables the
planner to estimate that17,000 LOC will be required for the
reports component. Similar estimates and computation are made
for other standard components, and a combined size value
(adjusted statistically) results.
Standard Component
Sizing
25. This approach is used when a project
encompasses the use of existing software that
must be modified in some way as part of a
project.
The planner estimates the number and type
(e.g., reuse, adding code, changing code,
deleting code) of modifications that must be
accomplished. Using
an “effort ratio” [PUT92] for each type of
change, the size of the change may be
estimated.
Change Sizing
26.
LOC and FP data are used in two ways during
software project estimation:
(1) as an estimation variable to "size“ each
element of the software and
(2) as baseline metrics collected from past
projects and used in conjunction with
estimation variables to develop cost and effort
projections.
Problem-Based
Estimation
27.
Baseline productivity metrics (e.g., LOC/pm
or FP/pm9) are then applied to the
appropriate estimation variable, and cost or
effort for the function is derived.
Function estimates are combined to produce
an overall estimate for the entire project.
Problem-Based
Estimation
28.
The LOC and FP estimation techniques differ
in the level of detail required for
decomposition and the target of the
partitioning.
When LOC is used as the estimation variable,
decomposition10 is absolutely essential and
is often taken to considerable levels of detail.
LOC and FP Estimation
29.
Software engineering activities are contracted
to a third party who does the work at lower
cost and, hopefully, higher quality. Software
work conducted within a company is reduced
to a contract management activity.
Outsourcing
30.
Process-based estimation begins with a
delineation of software functions obtained from
the project scope.
A series of software process activities must be
performed for each function.
Process-Based
Estimation
31.
Functions and related software process activities may
be represented as part of a table similar to the one
presented.
Functions- Software
Process Activities
32.
If process-based estimation is performed
independently of LOC or FP estimation, we
now have two or three estimates for cost and
effort that may be compared and reconciled.
If both sets of estimates show reasonable
agreement, there is good reason to believe
that the estimates are reliable.
Process-based
Estimation
33.
Estimates of effort (in person-months) for
each software engineering activity are
provided for each CAD software function
(abbreviated for brevity).
Example of Process-Based
Estimation
34.
Total estimated effort for the CAD software range
from a low of 46 person-months (derived using a
process-based estimation approach) to a high of 58
person-months(derived using an FP estimation
approach).
The average estimate (using all three approaches) is
53 person-months. The maximum variation from the
average estimate is approximately 13 percent.
Example of Process-Based
Estimation
35.
Divergent estimates can often be traced to one of two
causes:
1. The scope of the project is not adequately understood
or has been misinterpreted by the planner.
2. Productivity data used for problem-based estimation
techniques is inappropriate for the application, obsolete
(in that it no longer accurately reflects the software
engineering organization), or has been misapplied.
Example of Process-Based
Estimation
36. A typical estimation model is derived using
regression analysis on data collected from
past software projects. The overall structure of
such models takes the form
E = A + B x (ev)C
Where
A, B, and C are empirically derived constants,
E is effort in person-months, and
ev is the estimation variable (either LOC or FP).
Structure of Estimation
Models
37.
Many LOC-oriented estimation models
proposed in the literature are
E = 5.2 x (KLOC)0.91 Walston-Felix model
E = 5.5 + 0.73 x (KLOC)1.16 Bailey-Basili
model
E = 3.2 x (KLOC)1.05 Boehm simple model
E = 5.288 x (KLOC)1.047 Doty model for
KLOC > 9
LOC-Oriented Estimation
Models
38.
FP-oriented models have also been proposed
E = 13.39 + 0.0545 FP Albrecht and Gaffney
model
E = 60.62 x 7.728 x 10-8 FP3 Kemerer model
E = 585.7 + 15.12 FP Matson, Barnett, and
Mellichamp model
FP-Oriented Models
40.
At the strategic level, business
managers consider whether a
significant portion of all software
work can be contracted to others.
Strategic Level
41.
At the tactical level, a project
manager determines whether part or
all of a project can be best
accomplished by subcontracting the
software work.
Tactical Level
42.
Software engineering managers are faced with a
make/buy decision that can be further complicated by a
number of acquisition options:
(1) Software may be purchased (or licensed) off-the-
shelf,
(2) “fullexperience” or “partial-experience” software
components maybe acquired and then modified and
integrated to meet specific needs, or
(3) Software may be custom built by an outside
contractor to meet the purchaser's specifications.
Make/Buy Decision
43.
The software equation [PUT92] is a
dynamic multivariable model that
assumes a specific distribution of effort
over the life of a software development
project.
The model has been derived from
productivity data collected for over 4000
contemporary software projects.
Software Equation
44.
Based on these data, an estimation model of the form
E = [LOC B0.333/P]3 (1/t4) (5-3)
where E = effort in person-months or person-years
t = project duration in months or years
B = “special skills factor”16
P = “productivity parameter” that reflects:
• Overall process maturity and management practices
• The extent to which good software engineering practices are used
• The level of programming languages used
• The state of the software environment
• The skills and experience of the software team
• The complexity of the application
Estimation Model
45.
The estimation process and use a
more common form for their
estimation model, Putnam and
Myers.
Estimation Process
46.
Minimum development time is defined as
tmin = 8.14 (LOC/P)0.43 in months for tmin > 6 months (5-4a)
E = 180 Bt3 in person-months for E ≥ 20 person-months (5-4b)
Note that t in Equation (5-4b) is represented in years.
Using Equations (5-4) with P = 12,000 (the recommended value
for scientific software)
for the CAD software discussed earlier in this chapter,
tmin = 8.14 (33200/12000)0.43
tmin = 12.6 calendar months
E = 180 0.28 (1.05)3
E = 58 person-months
Estimation Process
Equation
48.
Project complexity has a strong effect on the uncertainty
inherent in planning. Complexity,however, is a relative
measure that is affected by familiarity with past effort.
The first-time developer of a sophisticated e-commerce
application might consider it to be exceedingly
complex.
Project Complexity
49.
It is important factor that can affect the accuracy and
efficacy of estimates. As size increases, the
interdependency among various elements of the
software grows rapidly.
Problem decomposition, an important approach to
estimating, becomes more difficult because
decomposed elements may still be formidable.
To paraphrase Murphy's law: "What can go wrong will
go wrong”—and if there are more things that can fail,
more things will fail.
Project Size
50.
It has an effect on estimation risk. In this context,
structure refers to the degree to which requirements
have been solidified, the ease with which functions can
be compartmentalized, and the hierarchical nature of
the information that must be processed.
Degree of Structural
Uncertainty
51.
Barry Boehm [BOE81] introduced a hierarchy of
software estimation models bearing the name
COCOMO, for
COnstructive COst Model
It has evolved into a more comprehensive estimation
model, called COCOMO II.
COCOMO Model
52. Application composition model-
Used during the early stages of software engineering, when
prototyping of user interfaces, consideration of software
and system interaction, assessment of performance, and
evaluation of technology maturity are paramount.
Early design stage model-
Used once requirements have been stabilized and basic
software architecture has been established.
Post-architecture-stage model-
Used during the construction of the software.
COCOMO II
53.
The COCOMO II application composition model uses
object points and is illustrated in the following
paragraphs.
COCOMO II
54.
The object point is an indirect software measure that is
computed using counts of the number of
(1) screens (at the user interface)
(2) reports,
(3) components likely to be required to build the
application.
Each object instance (e.g., a screen or report) is
classified into one of three complexity levels (i.e.,
simple, medium, or difficult) using criteria suggested
by Boehm.
Object Point
55.
Once complexity is determined, the number of screens,
reports, and components are weighted.
The object point count is then determined by
multiplying the original number of object instances by
the weighting factor and summing to obtain a total
object point count.
When component-based development or general
software reuse is to be applied, the percent of reuse
(%reuse) is estimated and the object point count is
adjusted:
Estimated Effort
56.
NOP = (object points) x [(100 %reuse)/100]
where NOP is defined as new object points.
To derive an estimate of effort based on the computed
NOP value, a “productivity rate” must be derived.
Table 5.2 presents the productivity rate
PROD = NOP/person-month
Estimated Effort
58.
Once the productivity rate has been determined, an
estimate of project effort can be derived as
Estimated effort = NOP/PROD
Estimated Effort
59.
The software equation [PUT92] is a dynamic multivariable
model that assumes a specific
distribution of effort over the life of a software
development project.
An estimation model of the form
E = [LOC B0.333/P]3 (1/t4)
Where
E = effort in person-months or person-years
t = project duration in months or years
B = “special skills factor”16
P = “productivity parameter”
Software Equation
60.
Overall process maturity and management
practices
The extent to which good software
engineering practices are used
The level of programming languages used
The state of the software environment
The skills and experience of the software
team
The complexity of the application
Productivity Parameter
61.
It is important to note that the software
equation has two independent
parameters:
(1)An estimate of size (in LOC) and
(2)An indication of project duration in
calendar months or years.
Parameters Software
Equation
62.
Minimum development time is defined as
tmin = 8.14 (LOC/P)0.43 in months for tmin
> 6 months
E = 180 Bt3 in person-months for E ≥ 20
person-months
Minimum
Development Time
63.
with P = 12,000 for the CAD software
tmin = 8.14 (33200/12000)0.43
tmin = 12.6 calendar months
E = 180 0.28 (1.05)3
E = 58 person-months
Minimum
Development Time
64.
Halstead proposed metrics or length and volume
of a program based on the number of operators
and operands.
n1 is the number of distinct operators
n2 is the number of distinct operands
f1 is the number of occurrences of the jth most
frequent operator
f2 is the number of occurrences of the jth most
frequent operand
Halstead’s Measure
65.
Vocublary n of a program is defined as
n= n1 + n1
N1 = ∑ f1,j N2 = ∑ f2,j
N1 Total number of occurrence of different
operators in the program
N2 Total number of occurrence of different
operands in the program
The length of program is
N=N1+ N2
Halstead’s Measure
66.
The Volume V of program is defined as
V= Nlog2(n)
log2(n)is the number of bits needed to represent
every element in the program
N is the total occurrences of the different
elements.
Volume is used as size metric for a program.
Halstead’s Measure
67. n1 is the number of unique operators
n2 is the number of unique operands
N1 is total frequency of operator
N2 is total frequency of operand
n1 /2 relative level of difficulty due to the larger
number of operators in the program
N1 / n2 represents the average number of times an
operand is used
Halstead’s Measure
68.
Ease of reading and writing is defined by
D= N1 *n2 /2 * n2
Halstead’s Measure
69.
Software Engineering by Somerville
Software Engineering-Pressman
Software Engineering- Pankaj Jalote
Software Engineering Tutorial Point
Software Engineering Wikipedia
REFERENCES