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.
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.
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
Application of Genetic Algorithm in Software Engineering: A ReviewIRJESJOURNAL
Abstract. The software engineering is comparatively new and regularly changing field. The big challenge of meeting strict project schedules with high quality software requires that the field of software engineering be automated to large extent and human resource intervention be minimized to optimum level. To achieve this goal the researcher have explored the potential of machine learning approaches as they are adaptable, have learning ability. In this paper, we take a look at how genetic algorithm (GA) can be used to build tool for software development and maintenance tasks.
ENSEMBLE REGRESSION MODELS FOR SOFTWARE DEVELOPMENT EFFORT ESTIMATION: A COMP...ijseajournal
As demand for computer software continually increases, software scope and complexity become higher than ever. The software industry is in real need of accurate estimates of the project under development. Software development effort estimation is one of the main processes in software project management. However, overestimation and underestimation may cause the software industry loses. This study determines which technique has better effort prediction accuracy and propose combined techniques that could provide better estimates. Eight different ensemble models to estimate effort with Ensemble Models were compared with each other base on the predictive accuracy on the Mean Absolute Residual (MAR) criterion and statistical tests. The results have indicated that the proposed ensemble models, besides delivering high efficiency in contrast to its counterparts, and produces the best responses for software project effort estimation. Therefore, the proposed ensemble models in this study will help the project managers working with development quality software.
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.
APPLYING REQUIREMENT BASED COMPLEXITY FOR THE ESTIMATION OF SOFTWARE DEVELOPM...cscpconf
The need of computing the software complexity in requirement analysis phase of software
development life cycle (SDLC) would be an enormous benefit for estimating the required
development and testing effort for yet to be developed software. Also, a relationship between
source code and difficulty in developing a source code are also attempted in order to estimate the
complexity of the proposed software for cost estimation, man power build up, code and
developer’s evaluation. Therefore, this paper presents a systematic and an integrated approach
for the estimation of software development and testing effort on the basis of improved
requirement based complexity (IRBC) of the proposed software. The IRBC measure serves as the
basis for estimation of these software development activities to enable the developers and
practitioners to predict the critical information about the software development intricacies and
obtained from software requirement specification (SRS) of proposed software. Hence, this paper
presents an integrated approach, for the prediction of software development and testing effort
using IRBC. For validation purpose, the proposed measures are categorically compared with
various established and prevalent practices proposed in the past. Finally, the results obtained, validates the claim, for the approaches discussed in this paper, for estimation of software development and testing effort, in the early phases of SDLC appears to be robust, comprehensive, early alarming and compares well with other measures proposed in the past.
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.
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.
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
Application of Genetic Algorithm in Software Engineering: A ReviewIRJESJOURNAL
Abstract. The software engineering is comparatively new and regularly changing field. The big challenge of meeting strict project schedules with high quality software requires that the field of software engineering be automated to large extent and human resource intervention be minimized to optimum level. To achieve this goal the researcher have explored the potential of machine learning approaches as they are adaptable, have learning ability. In this paper, we take a look at how genetic algorithm (GA) can be used to build tool for software development and maintenance tasks.
ENSEMBLE REGRESSION MODELS FOR SOFTWARE DEVELOPMENT EFFORT ESTIMATION: A COMP...ijseajournal
As demand for computer software continually increases, software scope and complexity become higher than ever. The software industry is in real need of accurate estimates of the project under development. Software development effort estimation is one of the main processes in software project management. However, overestimation and underestimation may cause the software industry loses. This study determines which technique has better effort prediction accuracy and propose combined techniques that could provide better estimates. Eight different ensemble models to estimate effort with Ensemble Models were compared with each other base on the predictive accuracy on the Mean Absolute Residual (MAR) criterion and statistical tests. The results have indicated that the proposed ensemble models, besides delivering high efficiency in contrast to its counterparts, and produces the best responses for software project effort estimation. Therefore, the proposed ensemble models in this study will help the project managers working with development quality software.
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.
APPLYING REQUIREMENT BASED COMPLEXITY FOR THE ESTIMATION OF SOFTWARE DEVELOPM...cscpconf
The need of computing the software complexity in requirement analysis phase of software
development life cycle (SDLC) would be an enormous benefit for estimating the required
development and testing effort for yet to be developed software. Also, a relationship between
source code and difficulty in developing a source code are also attempted in order to estimate the
complexity of the proposed software for cost estimation, man power build up, code and
developer’s evaluation. Therefore, this paper presents a systematic and an integrated approach
for the estimation of software development and testing effort on the basis of improved
requirement based complexity (IRBC) of the proposed software. The IRBC measure serves as the
basis for estimation of these software development activities to enable the developers and
practitioners to predict the critical information about the software development intricacies and
obtained from software requirement specification (SRS) of proposed software. Hence, this paper
presents an integrated approach, for the prediction of software development and testing effort
using IRBC. For validation purpose, the proposed measures are categorically compared with
various established and prevalent practices proposed in the past. Finally, the results obtained, validates the claim, for the approaches discussed in this paper, for estimation of software development and testing effort, in the early phases of SDLC appears to be robust, comprehensive, early alarming and compares well with other measures proposed in the past.
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.
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 Impact of Software Complexity on Cost and Quality - A Comparative Analysi...ijseajournal
Early prediction of software quality is important for better software planning and controlling. In early
development phases, design complexity metrics are considered as useful indicators of software testing
effort and some quality attributes. Although many studies investigate the relationship between design
complexity and cost and quality, it is unclear what we have learned beyond the scope of individual studies.
This paper presented a systematic review on the influence of software complexity metrics on quality
attributes. We aggregated Spearman correlation coefficients from 59 different data sets from 57 primary
studies by a tailored meta-analysis approach. We found that fault proneness and maintainability are most
frequently investigated attributes. Chidamber & Kemerer metric suite is most frequently used but not all of
them are good quality attribute indicators. Moreover, the impact of these metrics is not different in
proprietary and open source projects. The result provides some implications for building quality model
across project type.
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.
A Software Measurement Using Artificial Neural Network and Support Vector Mac...ijseajournal
Today, Software measurement are based on various techniques such that neural network, Genetic
algorithm, Fuzzy Logic etc. This study involves the efficiency of applying support vector machine using
Gaussian Radial Basis kernel function to software measurement problem to increase the performance and
accuracy. Support vector machines (SVM) are innovative approach to constructing learning machines that
Minimize generalization error. There is a close relationship between SVMs and the Radial Basis Function
(RBF) classifiers. Both have found numerous applications such as in optical character recognition, object
detection, face verification, text categorization, and so on. The result demonstrated that the accuracy and
generalization performance of SVM Gaussian Radial Basis kernel function is better than RBFN. We also
examine and summarize the several superior points of the SVM compared with RBFN.
A DECISION SUPPORT SYSTEM FOR ESTIMATING COST OF SOFTWARE PROJECTS USING A HY...ijfcstjournal
One of the major challenges for software, nowadays, is software cost estimation. It refers to estimating the
cost of all activities including software development, design, supervision, maintenance and so on. Accurate
cost-estimation of software projects optimizes the internal and external processes, staff works, efforts and
the overheads to be coordinated with one another. In the management software projects, estimation must
be taken into account so that reduces costs, timing and possible risks to avoid project failure. In this paper,
a decision- support system using a combination of multi-layer artificial neural network and decision tree is
proposed to estimate the cost of software projects. In the model included into the proposed system,
normalizing factors, which is vital in evaluating efforts and costs estimation, is carried out using C4.5
decision tree. Moreover, testing and training factors are done by multi-layer artificial neural network and
the most optimal values are allocated to them. The experimental results and evaluations on Dataset
NASA60 show that the proposed system has less amount of the total average relative error compared with
COCOMO model.
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
A Defect Prediction Model for Software Product based on ANFISIJSRD
Artificial intelligence techniques are day by day getting involvement in all the classification and prediction based process like environmental monitoring, stock exchange conditions, biomedical diagnosis, software engineering etc. However still there are yet to be simplify the challenges of selecting training criteria for design of artificial intelligence models used for prediction of results. This work focus on the defect prediction mechanism development using software metric data of KC1.We have taken subtractive clustering approach for generation of fuzzy inference system (FIS).The FIS rules are generated at different radius of influence of input attribute vectors and the developed rules are further modified by ANFIS technique to obtain the prediction of number of defects in software project using fuzzy logic system.
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.
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.
Comparative Performance Analysis of Machine Learning Techniques for Software ...csandit
Machine learning techniques can be used to analyse data from different perspectives and enable
developers to retrieve useful information. Machine learning techniques are proven to be useful
in terms of software bug prediction. In this paper, a comparative performance analysis of
different machine learning techniques is explored for software bug prediction on public
available data sets. Results showed most of the machine learning methods performed well on
software bug datasets.
SCHEDULING AND INSPECTION PLANNING IN SOFTWARE DEVELOPMENT PROJECTS USING MUL...ijseajournal
This paper presents a Multi-objective Hyper-heuristic Evolutionary Algorithm (MHypEA) for the solution
of Scheduling and Inspection Planning in Software Development Projects. Scheduling and Inspection
planning is a vital problem in software engineering whose main objective is to schedule the persons to
various activities in the software development process such as coding, inspection, testing and rework in
such a way that the quality of the software product is maximum and at the same time the project make span
and cost of the project are minimum. The problem becomes challenging when the size of the project is
huge. The MHypEA is an effective metaheuristic search technique for suggesting scheduling and inspection
planning. It incorporates twelve low-level heuristics which are based on different methods of selection,
crossover and mutation operations of Evolutionary Algorithms. The selection mechanism to select a lowlevel
heuristic is based on reinforcement learning with adaptive weights. The efficacy of the algorithm has
been studied on randomly generated test problem.
Efficient Indicators to Evaluate the Status of Software Development Effort Es...IJMIT JOURNAL
Development effort is an undeniable part of the project management which considerably influences the
success of project. Inaccurate and unreliable estimation of effort can easily lead to the failure of project.
Due to the special specifications, accurate estimation of effort in the software projects is a vital
management activity that must be carefully done to avoid from the unforeseen results. However numerous
effort estimation methods have been proposed in this field, the accuracy of estimates is not satisfying and
the attempts continue to improve the performance of estimation methods. Prior researches conducted in
this area have focused on numerical and quantitative approaches and there are a few research works that
investigate the root problems and issues behind the inaccurate effort estimation of software development
effort. In this paper, a framework is proposed to evaluate and investigate the situation of an organization in
terms of effort estimation. The proposed framework includes various indicators which cover the critical
issues in field of software development effort estimation. Since the capabilities and shortages of
organizations for effort estimation are not the same, the proposed indicators can lead to have a systematic
approach in which the strengths and weaknesses of organizations in field of effort estimation are
discovered
A DECISION SUPPORT SYSTEM FOR ESTIMATING COST OF SOFTWARE PROJECTS USING A HY...ijfcstjournal
One of the major challenges for software, nowadays, is software cost estimation. It refers to estimating the
cost of all activities including software development, design, supervision, maintenance and so on. Accurate
cost-estimation of software projects optimizes the internal and external processes, staff works, efforts and
the overheads to be coordinated with one another. In the management software projects, estimation must
be taken into account so that reduces costs, timing and possible risks to avoid project failure. In this paper,
a decision- support system using a combination of multi-layer artificial neural network and decision tree is
proposed to estimate the cost of software projects. In the model included into the proposed system,
normalizing factors, which is vital in evaluating efforts and costs estimation, is carried out using C4.5
decision tree. Moreover, testing and training factors are done by multi-layer artificial neural network and
the most optimal values are allocated to them. The experimental results and evaluations on Dataset
NASA60 show that the proposed system has less amount of the total average relative error compared with
COCOMO model.
A new model for software costestimationijfcstjournal
Accurate and realistic estimation is always considered to be a great challenge in software industry.
Software Cost Estimation (SCE) is the standard application used to manage software projects. Determining
the amount of estimation in the initial stages of the project depends on planning other activities of the
project. In fact, the estimation is confronted with a number of uncertainties and barriers’, yet assessing the
previous projects is essential to solve this problem. Several models have been developed for the analysis of
software projects. But the classical reference method is the COCOMO model, there are other methods
which are also applied such as Function Point (FP), Line of Code(LOC); meanwhile, the expert`s opinions
matter in this regard. In recent years, the growth and the combination of meta-heuristic algorithms with
high accuracy have brought about a great achievement in software engineering. Meta-heuristic algorithms
which can analyze data from multiple dimensions and identify the optimum solution between them are
analytical tools for the analysis of data. In this paper, we have used the Harmony Search (HS)algorithm for
SCE. The proposed model which is a collection of 60 standard projects from Dataset NASA60 has been
assessed.The experimental results show that HS algorithm is a good way for determining the weight
similarity measures factors of software effort, and reducing the error of MRE.
A NEW MODEL FOR SOFTWARE COSTESTIMATION USING HARMONY SEARCHijfcstjournal
Accurate and realistic estimation is always considered to be a great challenge in software industry.
Software Cost Estimation (SCE) is the standard application used to manage software projects. Determining
the amount of estimation in the initial stages of the project depends on planning other activities of the
project. In fact, the estimation is confronted with a number of uncertainties and barriers’, yet assessing the
previous projects is essential to solve this problem. Several models have been developed for the analysis of
software projects. But the classical reference method is the COCOMO model, there are other methods
which are also applied such as Function Point (FP), Line of Code(LOC); meanwhile, the expert`s opinions
matter in this regard. In recent years, the growth and the combination of meta-heuristic algorithms with
high accuracy have brought about a great achievement in software engineering. Meta-heuristic algorithms
which can analyze data from multiple dimensions and identify the optimum solution between them are
analytical tools for the analysis of data. In this paper, we have used the Harmony Search (HS)algorithm for
SCE. The proposed model which is a collection of 60 standard projects from Dataset NASA60 has been
assessed.The experimental results show that HS algorithm is a good way for determining the weight
similarity measures factors of software effort, and reducing the error of MRE.
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 Impact of Software Complexity on Cost and Quality - A Comparative Analysi...ijseajournal
Early prediction of software quality is important for better software planning and controlling. In early
development phases, design complexity metrics are considered as useful indicators of software testing
effort and some quality attributes. Although many studies investigate the relationship between design
complexity and cost and quality, it is unclear what we have learned beyond the scope of individual studies.
This paper presented a systematic review on the influence of software complexity metrics on quality
attributes. We aggregated Spearman correlation coefficients from 59 different data sets from 57 primary
studies by a tailored meta-analysis approach. We found that fault proneness and maintainability are most
frequently investigated attributes. Chidamber & Kemerer metric suite is most frequently used but not all of
them are good quality attribute indicators. Moreover, the impact of these metrics is not different in
proprietary and open source projects. The result provides some implications for building quality model
across project type.
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.
A Software Measurement Using Artificial Neural Network and Support Vector Mac...ijseajournal
Today, Software measurement are based on various techniques such that neural network, Genetic
algorithm, Fuzzy Logic etc. This study involves the efficiency of applying support vector machine using
Gaussian Radial Basis kernel function to software measurement problem to increase the performance and
accuracy. Support vector machines (SVM) are innovative approach to constructing learning machines that
Minimize generalization error. There is a close relationship between SVMs and the Radial Basis Function
(RBF) classifiers. Both have found numerous applications such as in optical character recognition, object
detection, face verification, text categorization, and so on. The result demonstrated that the accuracy and
generalization performance of SVM Gaussian Radial Basis kernel function is better than RBFN. We also
examine and summarize the several superior points of the SVM compared with RBFN.
A DECISION SUPPORT SYSTEM FOR ESTIMATING COST OF SOFTWARE PROJECTS USING A HY...ijfcstjournal
One of the major challenges for software, nowadays, is software cost estimation. It refers to estimating the
cost of all activities including software development, design, supervision, maintenance and so on. Accurate
cost-estimation of software projects optimizes the internal and external processes, staff works, efforts and
the overheads to be coordinated with one another. In the management software projects, estimation must
be taken into account so that reduces costs, timing and possible risks to avoid project failure. In this paper,
a decision- support system using a combination of multi-layer artificial neural network and decision tree is
proposed to estimate the cost of software projects. In the model included into the proposed system,
normalizing factors, which is vital in evaluating efforts and costs estimation, is carried out using C4.5
decision tree. Moreover, testing and training factors are done by multi-layer artificial neural network and
the most optimal values are allocated to them. The experimental results and evaluations on Dataset
NASA60 show that the proposed system has less amount of the total average relative error compared with
COCOMO model.
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
A Defect Prediction Model for Software Product based on ANFISIJSRD
Artificial intelligence techniques are day by day getting involvement in all the classification and prediction based process like environmental monitoring, stock exchange conditions, biomedical diagnosis, software engineering etc. However still there are yet to be simplify the challenges of selecting training criteria for design of artificial intelligence models used for prediction of results. This work focus on the defect prediction mechanism development using software metric data of KC1.We have taken subtractive clustering approach for generation of fuzzy inference system (FIS).The FIS rules are generated at different radius of influence of input attribute vectors and the developed rules are further modified by ANFIS technique to obtain the prediction of number of defects in software project using fuzzy logic system.
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.
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.
Comparative Performance Analysis of Machine Learning Techniques for Software ...csandit
Machine learning techniques can be used to analyse data from different perspectives and enable
developers to retrieve useful information. Machine learning techniques are proven to be useful
in terms of software bug prediction. In this paper, a comparative performance analysis of
different machine learning techniques is explored for software bug prediction on public
available data sets. Results showed most of the machine learning methods performed well on
software bug datasets.
SCHEDULING AND INSPECTION PLANNING IN SOFTWARE DEVELOPMENT PROJECTS USING MUL...ijseajournal
This paper presents a Multi-objective Hyper-heuristic Evolutionary Algorithm (MHypEA) for the solution
of Scheduling and Inspection Planning in Software Development Projects. Scheduling and Inspection
planning is a vital problem in software engineering whose main objective is to schedule the persons to
various activities in the software development process such as coding, inspection, testing and rework in
such a way that the quality of the software product is maximum and at the same time the project make span
and cost of the project are minimum. The problem becomes challenging when the size of the project is
huge. The MHypEA is an effective metaheuristic search technique for suggesting scheduling and inspection
planning. It incorporates twelve low-level heuristics which are based on different methods of selection,
crossover and mutation operations of Evolutionary Algorithms. The selection mechanism to select a lowlevel
heuristic is based on reinforcement learning with adaptive weights. The efficacy of the algorithm has
been studied on randomly generated test problem.
Efficient Indicators to Evaluate the Status of Software Development Effort Es...IJMIT JOURNAL
Development effort is an undeniable part of the project management which considerably influences the
success of project. Inaccurate and unreliable estimation of effort can easily lead to the failure of project.
Due to the special specifications, accurate estimation of effort in the software projects is a vital
management activity that must be carefully done to avoid from the unforeseen results. However numerous
effort estimation methods have been proposed in this field, the accuracy of estimates is not satisfying and
the attempts continue to improve the performance of estimation methods. Prior researches conducted in
this area have focused on numerical and quantitative approaches and there are a few research works that
investigate the root problems and issues behind the inaccurate effort estimation of software development
effort. In this paper, a framework is proposed to evaluate and investigate the situation of an organization in
terms of effort estimation. The proposed framework includes various indicators which cover the critical
issues in field of software development effort estimation. Since the capabilities and shortages of
organizations for effort estimation are not the same, the proposed indicators can lead to have a systematic
approach in which the strengths and weaknesses of organizations in field of effort estimation are
discovered
A DECISION SUPPORT SYSTEM FOR ESTIMATING COST OF SOFTWARE PROJECTS USING A HY...ijfcstjournal
One of the major challenges for software, nowadays, is software cost estimation. It refers to estimating the
cost of all activities including software development, design, supervision, maintenance and so on. Accurate
cost-estimation of software projects optimizes the internal and external processes, staff works, efforts and
the overheads to be coordinated with one another. In the management software projects, estimation must
be taken into account so that reduces costs, timing and possible risks to avoid project failure. In this paper,
a decision- support system using a combination of multi-layer artificial neural network and decision tree is
proposed to estimate the cost of software projects. In the model included into the proposed system,
normalizing factors, which is vital in evaluating efforts and costs estimation, is carried out using C4.5
decision tree. Moreover, testing and training factors are done by multi-layer artificial neural network and
the most optimal values are allocated to them. The experimental results and evaluations on Dataset
NASA60 show that the proposed system has less amount of the total average relative error compared with
COCOMO model.
A new model for software costestimationijfcstjournal
Accurate and realistic estimation is always considered to be a great challenge in software industry.
Software Cost Estimation (SCE) is the standard application used to manage software projects. Determining
the amount of estimation in the initial stages of the project depends on planning other activities of the
project. In fact, the estimation is confronted with a number of uncertainties and barriers’, yet assessing the
previous projects is essential to solve this problem. Several models have been developed for the analysis of
software projects. But the classical reference method is the COCOMO model, there are other methods
which are also applied such as Function Point (FP), Line of Code(LOC); meanwhile, the expert`s opinions
matter in this regard. In recent years, the growth and the combination of meta-heuristic algorithms with
high accuracy have brought about a great achievement in software engineering. Meta-heuristic algorithms
which can analyze data from multiple dimensions and identify the optimum solution between them are
analytical tools for the analysis of data. In this paper, we have used the Harmony Search (HS)algorithm for
SCE. The proposed model which is a collection of 60 standard projects from Dataset NASA60 has been
assessed.The experimental results show that HS algorithm is a good way for determining the weight
similarity measures factors of software effort, and reducing the error of MRE.
A NEW MODEL FOR SOFTWARE COSTESTIMATION USING HARMONY SEARCHijfcstjournal
Accurate and realistic estimation is always considered to be a great challenge in software industry.
Software Cost Estimation (SCE) is the standard application used to manage software projects. Determining
the amount of estimation in the initial stages of the project depends on planning other activities of the
project. In fact, the estimation is confronted with a number of uncertainties and barriers’, yet assessing the
previous projects is essential to solve this problem. Several models have been developed for the analysis of
software projects. But the classical reference method is the COCOMO model, there are other methods
which are also applied such as Function Point (FP), Line of Code(LOC); meanwhile, the expert`s opinions
matter in this regard. In recent years, the growth and the combination of meta-heuristic algorithms with
high accuracy have brought about a great achievement in software engineering. Meta-heuristic algorithms
which can analyze data from multiple dimensions and identify the optimum solution between them are
analytical tools for the analysis of data. In this paper, we have used the Harmony Search (HS)algorithm for
SCE. The proposed model which is a collection of 60 standard projects from Dataset NASA60 has been
assessed.The experimental results show that HS algorithm is a good way for determining the weight
similarity measures factors of software effort, and reducing the error of MRE.
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.
A simplified predictive framework for cost evaluation to fault assessment usi...IJECEIAES
Software engineering is an integral part of any software development scheme which frequently encounters bugs, errors, and faults. Predictive evaluation of software fault contributes towards mitigating this challenge to a large extent; however, there is no benchmarked framework being reported in this case yet. Therefore, this paper introduces a computational framework of the cost evaluation method to facilitate a better form of predictive assessment of software faults. Based on lines of code, the proposed scheme deploys adopts a machine-learning approach to address the perform predictive analysis of faults. The proposed scheme presents an analytical framework of the correlation-based cost model integrated with multiple standards machine learning (ML) models, e.g., linear regression, support vector regression, and artificial neural networks (ANN). These learning models are executed and trained to predict software faults with higher accuracy. The study considers assessing the outcomes based on error-based performance metrics in detail to determine how well each learning model performs and how accurate it is at learning. It also looked at the factors contributing to the training loss of neural networks. The validation result demonstrates that, compared to logistic regression and support vector regression, neural network achieves a significantly lower error score for software fault prediction.
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.
Enhancing software development cost control by forecasting the cost of rework...nooriasukmaningtyas
Industrial reports show massive cost overruns associated with software
projects. The cost of software reworks constitutes a large portion of the
overall cost, reflecting a substantial challenge in cost control. Earned value
management (EVM) is the most recognized model for project cost control.
However, it shows many limitations in forecasting the software project cost,
leading to a considerable challenge in cost control. Nevertheless, the major
EVM limitation found is its inability to forecast the cost of software rework.
This research investigated the factors affecting this limitation and suggests an
enhanced EVM model. The significant contribution of this research is its
incorporation of software-related factors into the EVM model. We
introduced the software rework index (SRI), which is incorporated into the
traditional EVM model to enhance its predictability of the software project
cost at completion, including the rework cost. We defined the SRI in terms of
two factors: product functional complexity and the team competency.
Finally, we evaluated the proposed model using a dataset drawn from five
actual projects. The results showed a significant enhancement in forecasting
accuracy.
Using Data Mining to Identify COSMIC Function Point Measurement Competence IJECEIAES
Cosmic Function Point (CFP) measurement errors leads budget, schedule and quality problems in software projects. Therefore, it’s important to identify and plan requirements engineers’ CFP training need quickly and correctly. The purpose of this paper is to identify software requirements engineers’ COSMIC Function Point measurement competence development need by using machine learning algorithms and requirements artifacts created by engineers. Used artifacts have been provided by a large service and technology company ecosystem in Telco. First, feature set has been extracted from the requirements model at hand. To do the data preparation for educational data mining, requirements and COSMIC Function Point (CFP) audit documents have been converted into CFP data set based on the designed feature set. This data set has been used to train and test the machine learning models by designing two different experiment settings to reach statistically significant results. Ten different machine learning algorithms have been used. Finally, algorithm performances have been compared with a baseline and each other to find the best performing models on this data set. In conclusion, REPTree, OneR, and Support Vector Machines (SVM) with Sequential Minimal Optimization (SMO) algorithms achieved top performance in forecasting requirements engineers’ CFP training need.
Automatically Estimating Software Effort and Cost using Computing Intelligenc...cscpconf
In the IT industry, precisely estimate the effort of each software project the development cost
and schedule are count for much to the software company. So precisely estimation of man
power seems to be getting more important. In the past time, the IT companies estimate the work
effort of man power by human experts, using statistics method. However, the outcomes are
always unsatisfying the management level. Recently it becomes an interesting topic if computing
intelligence techniques can do better in this field. This research uses some computing
intelligence techniques, such as Pearson product-moment correlation coefficient method and
one-way ANOVA method to select key factors, and K-Means clustering algorithm to do project
clustering, to estimate the software project effort. The experimental result show that using
computing intelligence techniques to estimate the software project effort can get more precise
and more effective estimation than using traditional human experts did
Analysis of software cost estimation usingijfcstjournal
The growing application of software and resource constraints in software projects development need a
more accurate estimate of the cost and effort because of the importance in program planning, coordinated
scheduling and resource management including the number of programming's and software design using
tools and modern methods of modeling. Effectively control of investment for software development is
achieved by accurate cost estimation.The accurate Software Cost Estimation (SCE) is very difficult in the
early stages of software development because many of input parameters that are effective in software's
effort are very vague and uncertain in the early stages. SCE that is the basis of software projects
development planning is considered to be of high accuracy, because if the estimate is less than actual
values, confidence factor is reduce and this is means the possibility of failure in project. Conversely, if the
project is estimated at more than the actual value it would be the concept of unhelpful investment and
waste of resources. In the evaluation of software projects is commonly used deterministic method. But
software world is totally different from the linear variables and nowadays for performance and estimation
should be used nonlinear and non-probabilistic methods. In this paper, we have studied the SCE Using
Fuzzy Logic (FL) and we have compared it with COCOMO model. Results of investigations show that FL is
a performance model for SCE.
FACTORS ON SOFTWARE EFFORT ESTIMATION ijseajournal
Software effort estimation is an important process of system development life cycle, as it may affect the
success of software projects if project designers estimate the projects inaccurately. In the past of few
decades, various effort prediction models have been proposed by academicians and practitioners.
Traditional estimation techniques include Lines of Codes (LOC), Function Point Analysis (FPA) method
and Mark II Function Points (Mark II FP) which have proven unsatisfactory for predicting effort of all
types of software. In this study, the author proposed a regression model to predict the effort required to
design small and medium scale application software. To develop such a model, the author used 60
completed software projects developed by a software company in Macau. From the projects, the author
extracted factors and applied them to a regression model. A prediction of software effort with accuracy of
MMRE = 8% was constructed.
Machine learning techniques can be used to analyse data from different perspectives and enable developers to retrieve useful information. Machine learning techniques are proven to be useful
in terms of software bug prediction. In this paper, a comparative performance analysis of
different machine learning techniques is explored for software bug prediction on public
available data sets. Results showed most of the machine learning methods performed well on
software bug datasets.
DETERMINING THE RISKY SOFTWARE PROJECTS USING ARTIFICIAL NEURAL NETWORKSijseajournal
Determining risky software projects early is a very important factor for project success. In this study it is aimed to choose the most correctly resulting modelling method that will be useful for early prediction of risky software projects to help companies to avoid losing time and money on unsuccessful projects and also facing legal requirements because of not being able to fullfill their responsibilites to their customers While making the research for this subject, it is seen that in previous researches, usually traditional modelling techniques were preferred. But it is observed that these methods were mostly resulted with high misclassification ratio. To overcome this problem, this study proposes a three-layered neural network (NN) architecture with a backpropagation algorithm. NN architecture was trained by using two different data sets which were OMRON data set (collected by OMRON) and 2016-2020 ES.LV data set (collected by the authors) separately. For the made of this study firstly the most relevant classification method (Gaussian Naive Bayes Algorithm) and the most relevant neural network method (Scaled Conjugate Gradient Backpropagation Algorithm) was chosen and both data sets were trained by using each method seperately for the purpose of observing which type of modelling architecture would give better results. Experimental results of this study showed that the neural network approach is useful for predicting whether a project is risky or not risky.
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.
A Defect Prediction Model for Software Product based on ANFISIJSRD
Artificial intelligence techniques are day by day getting involvement in all the classification and prediction based process like environmental monitoring, stock exchange conditions, biomedical diagnosis, software engineering etc. However still there are yet to be simplify the challenges of selecting training criteria for design of artificial intelligence models used for prediction of results. This work focus on the defect prediction mechanism development using software metric data of KC1.We have taken subtractive clustering approach for generation of fuzzy inference system (FIS).The FIS rules are generated at different radius of influence of input attribute vectors and the developed rules are further modified by ANFIS technique to obtain the prediction of number of defects in software project using fuzzy logic system.
A NEW HYBRID FOR SOFTWARE COST ESTIMATION USING PARTICLE SWARM OPTIMIZATION A...ieijjournal
Software Cost Estimation (SCE) is considered one of the most important sections in software engineering that results in capabilities and well-deserved influence on the processes of cost and effort. Two factors of cost and effort in software projects determine the success and failure of projects. The project that will be completed in a certain time and manpower is a successful one and will have good profit to project
managers. In most of the SCE techniques, algorithmic models such as COCOMO algorithm models have been used. COCOMO model is not capable of estimating the close approximations to the actual cost, because it runs in the form of linear. So, the models should be adapted that simultaneously with the number of Lines of Code (LOC) has the ability to estimate in a fair and accurate fashion for effort factors. Metaheuristic algorithms can be a good model for SCE due to the ability of local and global search. In this paper, we have used the hybrid of Particle Swarm Optimization (PSO) and Differential Evolution (DE) for the SCE. Test results on NASA60 software dataset show that the rate of Mean Magnitude of Relative Error (MMRE) error on hybrid model, in comparison with COCOMO model is reduced to about 9.55%
A NEW HYBRID FOR SOFTWARE COST ESTIMATION USING PARTICLE SWARM OPTIMIZATION A...ieijjournal1
Software Cost Estimation (SCE) is considered one of the most important sections in software engineering
that results in capabilities and well-deserved influence on the processes of cost and effort. Two factors of
cost and effort in software projects determine the success and failure of projects. The project that will be
completed in a certain time and manpower is a successful one and will have good profit to project
managers. In most of the SCE techniques, algorithmic models such as COCOMO algorithm models have
been used. COCOMO model is not capable of estimating the close approximations to the actual cost,
because it runs in the form of linear. So, the models should be adapted that simultaneously with the number
of Lines of Code (LOC) has the ability to estimate in a fair and accurate fashion for effort factors. Metaheuristic
algorithms can be a good model for SCE due to the ability of local and global search. In this
paper, we have used the hybrid of Particle Swarm Optimization (PSO) and Differential Evolution (DE) for
the SCE. Test results on NASA60 software dataset show that the rate of Mean Magnitude of Relative Error
(MMRE) error on hybrid model, in comparison with COCOMO model is reduced to about 9.55%.
ANALYSIS OF LAND SURFACE DEFORMATION GRADIENT BY DINSAR cscpconf
The progressive development of Synthetic Aperture Radar (SAR) systems diversify the exploitation of the generated images by these systems in different applications of geoscience. Detection and monitoring surface deformations, procreated by various phenomena had benefited from this evolution and had been realized by interferometry (InSAR) and differential interferometry (DInSAR) techniques. Nevertheless, spatial and temporal decorrelations of the interferometric couples used, limit strongly the precision of analysis results by these techniques. In this context, we propose, in this work, a methodological approach of surface deformation detection and analysis by differential interferograms to show the limits of this technique according to noise quality and level. The detectability model is generated from the deformation signatures, by simulating a linear fault merged to the images couples of ERS1 / ERS2 sensors acquired in a region of the Algerian south.
4D AUTOMATIC LIP-READING FOR SPEAKER'S FACE IDENTIFCATIONcscpconf
A novel based a trajectory-guided, concatenating approach for synthesizing high-quality image real sample renders video is proposed . The lips reading automated is seeking for modeled the closest real image sample sequence preserve in the library under the data video to the HMM predicted trajectory. The object trajectory is modeled obtained by projecting the face patterns into an KDA feature space is estimated. The approach for speaker's face identification by using synthesise the identity surface of a subject face from a small sample of patterns which sparsely each the view sphere. An KDA algorithm use to the Lip-reading image is discrimination, after that work consisted of in the low dimensional for the fundamental lip features vector is reduced by using the 2D-DCT.The mouth of the set area dimensionality is ordered by a normally reduction base on the PCA to obtain the Eigen lips approach, their proposed approach by[33]. The subjective performance results of the cost function under the automatic lips reading modeled , which wasn’t illustrate the superior performance of the
method.
MOVING FROM WATERFALL TO AGILE PROCESS IN SOFTWARE ENGINEERING CAPSTONE PROJE...cscpconf
Universities offer software engineering capstone course to simulate a real world-working environment in which students can work in a team for a fixed period to deliver a quality product. The objective of the paper is to report on our experience in moving from Waterfall process to Agile process in conducting the software engineering capstone project. We present the capstone course designs for both Waterfall driven and Agile driven methodologies that highlight the structure, deliverables and assessment plans.To evaluate the improvement, we conducted a survey for two different sections taught by two different instructors to evaluate students’ experience in moving from traditional Waterfall model to Agile like process. Twentyeight students filled the survey. The survey consisted of eight multiple-choice questions and an open-ended question to collect feedback from students. The survey results show that students were able to attain hands one experience, which simulate a real world-working environment. The results also show that the Agile approach helped students to have overall better design and avoid mistakes they have made in the initial design completed in of the first phase of the capstone project. In addition, they were able to decide on their team capabilities, training needs and thus learn the required technologies earlier which is reflected on the final product quality
PROMOTING STUDENT ENGAGEMENT USING SOCIAL MEDIA TECHNOLOGIEScscpconf
Using social media in education provides learners with an informal way for communication. Informal communication tends to remove barriers and hence promotes student engagement. This paper presents our experience in using three different social media technologies in teaching software project management course. We conducted different surveys at the end of every semester to evaluate students’ satisfaction and engagement. Results show that using social media enhances students’ engagement and satisfaction. However, familiarity with the tool is an important factor for student satisfaction.
A SURVEY ON QUESTION ANSWERING SYSTEMS: THE ADVANCES OF FUZZY LOGICcscpconf
In real world computing environment with using a computer to answer questions has been a human dream since the beginning of the digital era, Question-answering systems are referred to as intelligent systems, that can be used to provide responses for the questions being asked by the user based on certain facts or rules stored in the knowledge base it can generate answers of questions asked in natural , and the first main idea of fuzzy logic was to working on the problem of computer understanding of natural language, so this survey paper provides an overview on what Question-Answering is and its system architecture and the possible relationship and
different with fuzzy logic, as well as the previous related research with respect to approaches that were followed. At the end, the survey provides an analytical discussion of the proposed QA models, along or combined with fuzzy logic and their main contributions and limitations.
DYNAMIC PHONE WARPING – A METHOD TO MEASURE THE DISTANCE BETWEEN PRONUNCIATIONS cscpconf
Human beings generate different speech waveforms while speaking the same word at different times. Also, different human beings have different accents and generate significantly varying speech waveforms for the same word. There is a need to measure the distances between various words which facilitate preparation of pronunciation dictionaries. A new algorithm called Dynamic Phone Warping (DPW) is presented in this paper. It uses dynamic programming technique for global alignment and shortest distance measurements. The DPW algorithm can be used to enhance the pronunciation dictionaries of the well-known languages like English or to build pronunciation dictionaries to the less known sparse languages. The precision measurement experiments show 88.9% accuracy.
INTELLIGENT ELECTRONIC ASSESSMENT FOR SUBJECTIVE EXAMS cscpconf
In education, the use of electronic (E) examination systems is not a novel idea, as Eexamination systems have been used to conduct objective assessments for the last few years. This research deals with randomly designed E-examinations and proposes an E-assessment system that can be used for subjective questions. This system assesses answers to subjective questions by finding a matching ratio for the keywords in instructor and student answers. The matching ratio is achieved based on semantic and document similarity. The assessment system is composed of four modules: preprocessing, keyword expansion, matching, and grading. A survey and case study were used in the research design to validate the proposed system. The examination assessment system will help instructors to save time, costs, and resources, while increasing efficiency and improving the productivity of exam setting and assessments.
TWO DISCRETE BINARY VERSIONS OF AFRICAN BUFFALO OPTIMIZATION METAHEURISTICcscpconf
African Buffalo Optimization (ABO) is one of the most recent swarms intelligence based metaheuristics. ABO algorithm is inspired by the buffalo’s behavior and lifestyle. Unfortunately, the standard ABO algorithm is proposed only for continuous optimization problems. In this paper, the authors propose two discrete binary ABO algorithms to deal with binary optimization problems. In the first version (called SBABO) they use the sigmoid function and probability model to generate binary solutions. In the second version (called LBABO) they use some logical operator to operate the binary solutions. Computational results on two knapsack problems (KP and MKP) instances show the effectiveness of the proposed algorithm and their ability to achieve good and promising solutions.
DETECTION OF ALGORITHMICALLY GENERATED MALICIOUS DOMAINcscpconf
In recent years, many malware writers have relied on Dynamic Domain Name Services (DDNS) to maintain their Command and Control (C&C) network infrastructure to ensure a persistence presence on a compromised host. Amongst the various DDNS techniques, Domain Generation Algorithm (DGA) is often perceived as the most difficult to detect using traditional methods. This paper presents an approach for detecting DGA using frequency analysis of the character distribution and the weighted scores of the domain names. The approach’s feasibility is demonstrated using a range of legitimate domains and a number of malicious algorithmicallygenerated domain names. Findings from this study show that domain names made up of English characters “a-z” achieving a weighted score of < 45 are often associated with DGA. When a weighted score of < 45 is applied to the Alexa one million list of domain names, only 15% of the domain names were treated as non-human generated.
GLOBAL MUSIC ASSET ASSURANCE DIGITAL CURRENCY: A DRM SOLUTION FOR STREAMING C...cscpconf
The amount of piracy in the streaming digital content in general and the music industry in specific is posing a real challenge to digital content owners. This paper presents a DRM solution to monetizing, tracking and controlling online streaming content cross platforms for IP enabled devices. The paper benefits from the current advances in Blockchain and cryptocurrencies. Specifically, the paper presents a Global Music Asset Assurance (GoMAA) digital currency and presents the iMediaStreams Blockchain to enable the secure dissemination and tracking of the streamed content. The proposed solution provides the data owner the ability to control the flow of information even after it has been released by creating a secure, selfinstalled, cross platform reader located on the digital content file header. The proposed system provides the content owners’ options to manage their digital information (audio, video, speech, etc.), including the tracking of the most consumed segments, once it is release. The system benefits from token distribution between the content owner (Music Bands), the content distributer (Online Radio Stations) and the content consumer(Fans) on the system blockchain.
IMPORTANCE OF VERB SUFFIX MAPPING IN DISCOURSE TRANSLATION SYSTEMcscpconf
This paper discusses the importance of verb suffix mapping in Discourse translation system. In
discourse translation, the crucial step is Anaphora resolution and generation. In Anaphora
resolution, cohesion links like pronouns are identified between portions of text. These binders
make the text cohesive by referring to nouns appearing in the previous sentences or nouns
appearing in sentences after them. In Machine Translation systems, to convert the source
language sentences into meaningful target language sentences the verb suffixes should be
changed as per the cohesion links identified. This step of translation process is emphasized in
the present paper. Specifically, the discussion is on how the verbs change according to the
subjects and anaphors. To explain the concept, English is used as the source language (SL) and
an Indian language Telugu is used as Target language (TL)
EXACT SOLUTIONS OF A FAMILY OF HIGHER-DIMENSIONAL SPACE-TIME FRACTIONAL KDV-T...cscpconf
In this paper, based on the definition of conformable fractional derivative, the functional
variable method (FVM) is proposed to seek the exact traveling wave solutions of two higherdimensional
space-time fractional KdV-type equations in mathematical physics, namely the
(3+1)-dimensional space–time fractional Zakharov-Kuznetsov (ZK) equation and the (2+1)-
dimensional space–time fractional Generalized Zakharov-Kuznetsov-Benjamin-Bona-Mahony
(GZK-BBM) equation. Some new solutions are procured and depicted. These solutions, which
contain kink-shaped, singular kink, bell-shaped soliton, singular soliton and periodic wave
solutions, have many potential applications in mathematical physics and engineering. The
simplicity and reliability of the proposed method is verified.
AUTOMATED PENETRATION TESTING: AN OVERVIEWcscpconf
The using of information technology resources is rapidly increasing in organizations,
businesses, and even governments, that led to arise various attacks, and vulnerabilities in the
field. All resources make it a must to do frequently a penetration test (PT) for the environment
and see what can the attacker gain and what is the current environment's vulnerabilities. This
paper reviews some of the automated penetration testing techniques and presents its
enhancement over the traditional manual approaches. To the best of our knowledge, it is the
first research that takes into consideration the concept of penetration testing and the standards
in the area.This research tackles the comparison between the manual and automated
penetration testing, the main tools used in penetration testing. Additionally, compares between
some methodologies used to build an automated penetration testing platform.
CLASSIFICATION OF ALZHEIMER USING fMRI DATA AND BRAIN NETWORKcscpconf
Since the mid of 1990s, functional connectivity study using fMRI (fcMRI) has drawn increasing
attention of neuroscientists and computer scientists, since it opens a new window to explore
functional network of human brain with relatively high resolution. BOLD technique provides
almost accurate state of brain. Past researches prove that neuro diseases damage the brain
network interaction, protein- protein interaction and gene-gene interaction. A number of
neurological research paper also analyse the relationship among damaged part. By
computational method especially machine learning technique we can show such classifications.
In this paper we used OASIS fMRI dataset affected with Alzheimer’s disease and normal
patient’s dataset. After proper processing the fMRI data we use the processed data to form
classifier models using SVM (Support Vector Machine), KNN (K- nearest neighbour) & Naïve
Bayes. We also compare the accuracy of our proposed method with existing methods. In future,
we will other combinations of methods for better accuracy.
VALIDATION METHOD OF FUZZY ASSOCIATION RULES BASED ON FUZZY FORMAL CONCEPT AN...cscpconf
In order to treat and analyze real datasets, fuzzy association rules have been proposed. Several
algorithms have been introduced to extract these rules. However, these algorithms suffer from
the problems of utility, redundancy and large number of extracted fuzzy association rules. The
expert will then be confronted with this huge amount of fuzzy association rules. The task of
validation becomes fastidious. In order to solve these problems, we propose a new validation
method. Our method is based on three steps. (i) We extract a generic base of non redundant
fuzzy association rules by applying EFAR-PN algorithm based on fuzzy formal concept analysis.
(ii) we categorize extracted rules into groups and (iii) we evaluate the relevance of these rules
using structural equation model.
PROBABILITY BASED CLUSTER EXPANSION OVERSAMPLING TECHNIQUE FOR IMBALANCED DATAcscpconf
In many applications of data mining, class imbalance is noticed when examples in one class are
overrepresented. Traditional classifiers result in poor accuracy of the minority class due to the
class imbalance. Further, the presence of within class imbalance where classes are composed of
multiple sub-concepts with different number of examples also affect the performance of
classifier. In this paper, we propose an oversampling technique that handles between class and
within class imbalance simultaneously and also takes into consideration the generalization
ability in data space. The proposed method is based on two steps- performing Model Based
Clustering with respect to classes to identify the sub-concepts; and then computing the
separating hyperplane based on equal posterior probability between the classes. The proposed
method is tested on 10 publicly available data sets and the result shows that the proposed
method is statistically superior to other existing oversampling methods.
CHARACTER AND IMAGE RECOGNITION FOR DATA CATALOGING IN ECOLOGICAL RESEARCHcscpconf
Data collection is an essential, but manpower intensive procedure in ecological research. An
algorithm was developed by the author which incorporated two important computer vision
techniques to automate data cataloging for butterfly measurements. Optical Character
Recognition is used for character recognition and Contour Detection is used for imageprocessing.
Proper pre-processing is first done on the images to improve accuracy. Although
there are limitations to Tesseract’s detection of certain fonts, overall, it can successfully identify
words of basic fonts. Contour detection is an advanced technique that can be utilized to
measure an image. Shapes and mathematical calculations are crucial in determining the precise
location of the points on which to draw the body and forewing lines of the butterfly. Overall,
92% accuracy were achieved by the program for the set of butterflies measured.
SOCIAL MEDIA ANALYTICS FOR SENTIMENT ANALYSIS AND EVENT DETECTION IN SMART CI...cscpconf
Smart cities utilize Internet of Things (IoT) devices and sensors to enhance the quality of the city
services including energy, transportation, health, and much more. They generate massive
volumes of structured and unstructured data on a daily basis. Also, social networks, such as
Twitter, Facebook, and Google+, are becoming a new source of real-time information in smart
cities. Social network users are acting as social sensors. These datasets so large and complex
are difficult to manage with conventional data management tools and methods. To become
valuable, this massive amount of data, known as 'big data,' needs to be processed and
comprehended to hold the promise of supporting a broad range of urban and smart cities
functions, including among others transportation, water, and energy consumption, pollution
surveillance, and smart city governance. In this work, we investigate how social media analytics
help to analyze smart city data collected from various social media sources, such as Twitter and
Facebook, to detect various events taking place in a smart city and identify the importance of
events and concerns of citizens regarding some events. A case scenario analyses the opinions of
users concerning the traffic in three largest cities in the UAE
SOCIAL NETWORK HATE SPEECH DETECTION FOR AMHARIC LANGUAGEcscpconf
The anonymity of social networks makes it attractive for hate speech to mask their criminal
activities online posing a challenge to the world and in particular Ethiopia. With this everincreasing
volume of social media data, hate speech identification becomes a challenge in
aggravating conflict between citizens of nations. The high rate of production, has become
difficult to collect, store and analyze such big data using traditional detection methods. This
paper proposed the application of apache spark in hate speech detection to reduce the
challenges. Authors developed an apache spark based model to classify Amharic Facebook
posts and comments into hate and not hate. Authors employed Random forest and Naïve Bayes
for learning and Word2Vec and TF-IDF for feature selection. Tested by 10-fold crossvalidation,
the model based on word2vec embedding performed best with 79.83%accuracy. The
proposed method achieve a promising result with unique feature of spark for big data.
GENERAL REGRESSION NEURAL NETWORK BASED POS TAGGING FOR NEPALI TEXTcscpconf
This article presents Part of Speech tagging for Nepali text using General Regression Neural
Network (GRNN). The corpus is divided into two parts viz. training and testing. The network is
trained and validated on both training and testing data. It is observed that 96.13% words are
correctly being tagged on training set whereas 74.38% words are tagged correctly on testing
data set using GRNN. The result is compared with the traditional Viterbi algorithm based on
Hidden Markov Model. Viterbi algorithm yields 97.2% and 40% classification accuracies on
training and testing data sets respectively. GRNN based POS Tagger is more consistent than the
traditional Viterbi decoding technique.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
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Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
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Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
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Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
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Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
2. 378 Computer Science & Information Technology (CS & IT)
direct impact for causing damage to software companies. Since estimation accuracy is largely
affected by modeling accuracy, finding good models for software estimation are now one of the
most important objectives of the software engineering community [5]. 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) [6]. Among IS,
Artificial Neural Network and Fuzzy Logic are the two most popular soft computing techniques
for software development effort estimation.
Since the last two decades, Artificial Neural Network (ANN) are being used extensively for
predictions in diverse applications and the neural networks are recognized for their ability to
produce reasonably accurate predictions in situations where complex relationships between inputs
and outputs exist and where the input data is distorted by high noise levels [7]. Hughes [8], Wittig
and Finnie [9][10] and Idri et al. [11] have employed neural network to predict the development
effort on different data sets.
Many researchers have worked and proposed SCE models based on the Fuzzy Logic Techniques.
Fei and Liu, [12] introduced the f-COCOMO model which applied Fuzzy Logic to the COCOMO
model for software effort estimation. Kumar et al, [13] had applied fuzzy logic in Putnam’s
manpower buildup index (MBI) estimation model. Ryder [14] researched on the application of
fuzzy logic to COCOMO and Function Points models. His result showed Fuzzy Logic is good at
making effort estimations.
1.1. Early Stage Software Development
Early stage effort estimations can be defined as making software development effort
estimations at the initial stages more precisely the Design stage of SDLC. Carrying out
effort estimations at the early stages is beneficial because the design stage prediction
implies fewer overheads at the later stages of software development. Figure 1 below
signifies that the total project effort comprises of the efforts (given in percentage) which goes into
surpassing each of the individual phases. It is evident from the Figure 1 that most of the efforts
(nearly 60 per cent) are spread over two initial phases of Analysis and Design. Hence if the
accurate effort requirements can be predicted from the initial or early phases of the SDLC, then
an efficient project development schedule can easily be prepared so as to complete the project
well within the targeted time and budget constraints.
Figure 1: Effort distribution in the individual phases of SDLC
(Source: Peter Müller – Software Engineering, SS 2006)
The state of the art literature has revealed that not much work on estimating the effort required for
software project development at the early stages in the Software Development Life Cycle (SDLC)
3. Computer Science & Information Technology (CS & IT) 379
has been done. Thus, this area still remains open to attract researchers to develop and propose
new models for early stage effort estimation.
2. EXPERIMENTAL METHODOLOGY
For carrying out the effort prediction in the early stages of software development, precisely in the
design phase of SDLC, a student dataset was prepared based on the Entity Relationship Diagrams
(ERDs) generated by the final year B.Tech. degree students of Computer Science & Engineering
Department of Sikkim Manipal Institute of Technology, India, as part of their Major Project work
spanning 16 weeks duration. Total Count of Entities (TCOE), Total Count of Attributes (TCOA),
Total Count of Relationships (TCOR), Cumulative Grade Point Aggregate (CGPA) and Major
Project final marks have been considered as explanatory variables in the dataset. The relevant
data of students of different batches have been gathered. The final marks obtained by students in
the Major Project are used to obtain the Recalculated Development Effort (RDE) in number of
weeks (effort) of software development.
In a previous work [15] carried out by the authors of this paper, a comparison of different neural
networks was carried out to predict the effort estimation at the early stages of software
development. In the work the Development Time (DT) was obtained by applying various
methods such as the Feed Forward Back Propagation Neural Network model, Cascaded Feed
Forward Back Propagation Neural Network (CFFBPNN) model, Elman Back Propagation Neural
Network (EBPNN) model, Layer Recurrent Neural Network (LRNN) model and Generalized
Regression Neural Network (GRNN) model with the help of Neural Network toolbox of
MATLAB R2007b software. The performances were then compared in terms of MMRE, Pred
(0.25), BRE% etc. All these models were trained with first 31 inputs from the dataset and later the
models were tested with 10 inputs from the same dataset.
In another work [16], Mamdani FIS from the Fuzzy logic toolbox of Matlab 7.0 was applied on
the student dataset as given in Annexure II, Table 3, to evaluate the efficiency of the FIS in
estimating the efforts in the early stages of SDLC. For experimentation from the dataset, the Total
count of Entities (TCOE), Cumulative Grade Point Aggregate (CGPA) have been taken as two
input variables and Redistributed Development Effort (RDE) as the output variable for preparing
Mamdani FIS.
In the present paper a comparison of the performance of different neural network models with
Mamdani FIS is done. For the experiments the same student dataset was used and models were
applied on to the dataset. A comparison of the MMRE values obtained from calculating the
Redistributed Effort Estimations (RDE’s) after employing the neural networks and fuzzy logic on
the dataset was carried out to evaluate the efficiency of the better of the two in estimating effort
estimation at the early stage of effort estimation.
2.1. Evaluation Criteria
There are many evaluation criteria to evaluate the accuracy of the software development
effort in literature. The Mean Magnitude Relative error (MMRE) is a widely-accepted
criterion in the literature and is based on the calculation of the magnitude relative error (MRE).
Eq. (1) as below shows an equation for computing the MRE value that is used to assess the
accuracies of the effort estimates.
4. 380 Computer Science & Information Technology (CS & IT)
Eq. (1)
The MRE calculates each project in a dataset while the MMRE aggregates the multiple projects.
The model with the lowest MMRE is considered the best [4].
3. RESULTS AND DISCUSSIONS
The values of MMRE are calculated for each of the neural networks and fuzzy logic are as shown
in Annexure I, Table 2 and Annexure III, Table 4 respectively. The results obtained after
comparing the RDE values are graphically shown in Figure 2 and their values are listed in Table
1.
Table 1 Comparison of different neural networks and Mamdani FIS based on MMRE values
12.96
13.59
11.45
3.89
0
2
4
6
8
10
12
14
16
FFBPNN Cascaded
FFBPNN
LRNN Mamdani FIS
MMRE
Figure 2: Comparison of MMRE values of neural network and fuzzy logic
4. CONCLUSION
It is evident from the Figure 2 that the Linear Regression Neural network (LRNN) has the lowest
value for MMRE among the other neural network models but when it is compared with fuzzy
5. Computer Science & Information Technology (CS & IT) 381
logic, it is observed that fuzzy logic outperforms neural network models as it has the lowest
MMRE value. Thus, fuzzy logic is the best model for predicting early stage effort estimation.
REFERENCES
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[2] Lederer, A.L.; Prasad, J., ‘Nine Management Guidelines for Better Cost Estimating’,
Communications of the ACM. 35, 2, 51 – 59, 1992.
[3] Moløkken, K.; Jørgensen, M., ‘A review of surveys on software effort estimation’, International
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networks', Computer Engineering and Technology (ICCET), 2nd International Conference
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[5] Huang, X.; Capretz. L.F.; Ren, J.; Ho D.A., ‘Neuro-Fuzzy Model for Software Cost Estimation’,
Proceedings of the Third International Conference on Quality Software, 2003.
[6] Grimstad, S., Jorgensen, M., Molokken-Ostvold, K., Software Effort Estimation Terminology:
The Tower of Babel. Information and Software Technology. Elsevier, 2005.
[7] Park, H.; Baek, S., 'An empirical validation of a neural network model for software effort estimation,'
Expert Systems with Applications, vol. 35, no. 3, pp. 929–937, 2008.
[8] Hughes, R.T., 'An Evaluation of Machine Learning Techniques for Software Effort Estimation',
University of Brighton, 1996.
[9] Wittig, G.; Finnie, G., ‘Estimating Software Development Effort with Connectionist Models’,
Information and Software Technology. Volume 39, 469-476, 1997.
[10] Witting, G.; Finnie, G., “Using Artificial Neural Networks and Function Points to Estimate 4GL
Software Development Effort”, J. Information Systems, vol.1, no.2, pp.87-94, 1994.
[11] Idri, A.; Khoshgoftaar, T.M.; Abran, A., “Can neural networks be easily interpreted in software cost
estimation?” IEEE Trans. Software Engineering, Vol. 2, pp. 1162 – 1167, 2002.
[12] Fei, Z; Liu, X., ‘f-COCOMO Fuzzy Constructive Cost Model in Software Engineering’, IEEE
international conference on Fuzzy systems, pp. 331-337, 1992.
[13] Kumar, S.; Krishna, B.A.; Satsangi, P.S., "Fuzzy systems and neural networks in software
engineering project management”, Journal of Applied Intelligence, Vol. 4, pp. 31-52, 1994.
[14] Ryder, J., “Fuzzy Modeling of Software Effort Prediction” in Proceeding. of IEEE Information
Technology Conference, Syracuse, NY, pp: 53-56, 1-3 Sept 1998.
[15] Bhatnagar, R.; Ghose, M.K.; Bhattacharjee, V., “A novel approach to the Early Stage Software
Development Effort Estimations using Neural Network Models: a Case Study”; Artificial Intelligence
Techniques - Novel Approaches & Practical Applications” of International Journal of Computer
Applications (USA), Number 3 - Article 5, 2011 pp 27-30.
[16] Bhatnagar, R.; Ghose, M.K.; Bhattacharjee, V., “Predicting the Early Stage Software Development
Effort using Mamdani FIS", International Journal of Computer Science and Information Technologies
(IJCSIT), Vol. 2 (4) , 1675-1678, 2011. ISSN: 0975-9646
8. 384 Computer Science & Information Technology (CS & IT)
37 9 6.79 70 70 0.000
38 8 8.095 65 75 0.154
39 20 7.99 75 75 0.000
40 20 8.095 75 75 0.000
41 15 6.34 75 71 0.053
Authors
Dr. Roheet Bhatnagar received his B.Tech. in Computer Science and Engineering
and M.Tech. in Remote Sensing from Birla Institute of Technology, Mesra,
Ranchi, India in 1996 and 2004 respectively and PhD in Computer Science &
Engineering from Sikkim Manipal University in 2011. He is having more than 14
years of varied experience in the software industries and academics. He had worked
in multinationals viz; Xerox Modicorp Ltd., Samsung SDS India Pvt. Ltd. and
USHA Soft (a software subsidiary of USHA Martin Ltd.) in Gurgaon from 1997 till
2003 just after his graduation. During his stint in the industry he had a good
exposure to software development executing many projects with different roles and
responsibilities. He joined Department of Remote Sensing at BIT Mesra, Ranchi in the year 2003 and
worked as Assistant Professor till 2008. He joined Sikkim Manipal Institute of Technology (SMIT) - a
constituent college of Sikkim Manipal University (SMU) in 2008 and is presently serving as Associate
Professor in the Department of Computer Science and Engineering. He has a number of publications in
indexed international journals and national and international conferences. He is a life member of
professional societies like Indian Society of Remote Sensing (ISRS), Indian Society of Technical Education
(ISTE), and International Association of Engineers (IAENG). His current areas of interest are, soft
computing, fuzzy and neural networks, database management systems, data mining and knowledge
discovery, Remote Sensing and Geographical Information Systems (RS-GIS), and software engineering.
He can be reached at roheetbhatnagar@yahoo.com and roheet.bhatnagar@gmail.com
Prof. (Dr) Mrinal Kanti Ghose was born on 1st
March 1952. He is a PhD and specializes in Software
Engineering, Image Processing, Remote Sensing & GIS. His other Area of research are Artificial
Intelligence, Data Mining, Simulation & Modeling, Optimization & Genetic Algorithms.
Currently Prof. Ghose is working as Dean (R&D), SMIT and Professor & Head, Department of Computer
Science & Engineering at Sikkim Manipal Institute of Technology,
Sikkim, India. He is having vast experience of 32 years in teaching and research. During
his career he has been associated with many prestigious universities and organizations.
He had worked at Regional Engg. College ( NIT ), Silchar (1979 – 1981), Assam Central
University, Silchar as COE and HOD of Computer Science Department (1997-2000). He
was associated with Vikram Sarabhai Space Centre / ISRO, Thiruvananthapuram from
1981-1994 & Regional Remote Sensing Service Center / ISRO , Kharagpur from 1995 –
1996 and from 2000-2006. He was an Adjunct Professor, Reliability Engg Centre, IEM,
IIT Kharagpur, from 2000 – 2005. He has more than 95 research publications in reputed
National/International Journals and Conferences. He has written a number of Technical reports and co-
authored a couple of books. He has organized a number of Conferences, Workshops and Seminars. He has
guided a number of Master level students and guiding a number of PhD students. He has also worked on a
number of consultancy projects.