Software project planning and estimation is the most important confront for software developers a... more Software project planning and estimation is the most important confront for software developers and researchers. It incorporates estimating the size of the software project to be produced, estimating the effort required, developing initial project schedules, and ultimately, estimating on the whole cost of the project. Numerous empirical explorations have been performed on the existing methods, but they lack convergence in choosing the best prediction methodology. Analogy based estimation is still one of the most extensively used method in industry which is based on finding effort from similar projects from the project repository. Two alternative approaches using analogy for estimation have been proposed in this study. Firstly, a precise and comprehensible predictive model based on the integration of Grey Relational Analysis (GRA) and regression has been discussed. Second approach deals with the uncertainty in the software projects, and how fuzzy set theory in fusion with grey relati...
Journal of Statistics and Management Systems, 2011
There are available metrics for predicting fault prone classes, which may help software organizat... more There are available metrics for predicting fault prone classes, which may help software organizations for planning and performing testing activities. This may be possible due to proper allocation of resources on fault prone parts of the design and code of the software. Hence, importance and usefulness of such metrics is understandable, but empirical validation of these metrics is always a great challenge. J48 decision tree algorithm has been successfully applied for solving classi cation problems in many applications. This paper evaluates the capability of algorithm and compares its performance with nine statistical and machine learning methods in predicting fault prone software classes using publicly available NASA data set. The results indicate that the prediction performance of J48 is generally better than other statistical and machine learning models. However, similar types of studies are required to be carried out in order to establish the acceptability of the J48 model.
The aim of this paper is to rank the impact of Object Oriented(OO) metrics in fault prediction mo... more The aim of this paper is to rank the impact of Object Oriented(OO) metrics in fault prediction modeling using Artificial Neural Networks(ANNs). Past studies on empirical validation of object oriented metrics as fault predictors using ANNs have focused on the predictive quality of neural networks versus standard statistical techniques. In this empirical study we turn our attention to the capability of ANNs in ranking the impact of these explanatory metrics on fault proneness. In ANNs data analysis approach, there is no clear method of ranking the impact of individual metrics. Five ANN based techniques are studied which rank object oriented metrics in predicting fault proneness of classes. These techniques are i) overall connection weights method ii) Garson's method iii) The partial derivatives methods iv) The Input Perturb method v) the classical stepwise methods. We develop and evaluate different prediction models based on the ranking of the metrics by the individual techniques. The models based on overall connection weights and partial derivatives methods have been found to be most accurate.
Empirical validation of software metrics to predict quality using machine learning methods is imp... more Empirical validation of software metrics to predict quality using machine learning methods is important to ensure their practical relevance in the software organizations. In this paper, we build a Support Vector Machine (SVM) model to find the relationship between object-oriented metrics given by Chidamber and Kemerer and fault proneness. The proposed model is empirically evaluated using public domain KC1 NASA data set. The performance of the SVM method was evaluated by Receiver Operating Characteristic (ROC) analysis. Based on these results, it is reasonable to claim that such models could help for planning and performing testing by focusing resources on fault-prone parts of the design and code. Thus, the study shows that SVM method may also be used in constructing software quality models.
The importance of software measurement is increasing leading to development of new measurement te... more The importance of software measurement is increasing leading to development of new measurement techniques. As the development of object-oriented software is rising, more and more metrics are being defined for object-oriented languages. Many metrics have been proposed related to various object-oriented constructs like class, coupling, cohesion, inheritance, information hiding and polymorphism. The applicability of metrics developed by previous researchers is mostly limited to requirement, design and implementation phase. Exception handling is a desirable feature of software that leads to robust design and must be measured. This research addresses this need and introduces a new set of design metrics for object-oriented code. Two metrics are developed that measure the amount of robustness included in the code. The metrics are analytically evaluated against Weyuker's proposed set of nine axioms. These set of metrics are calculated and analyzed for standard projects and accordingly ways in which project managers can utilize these metrics are suggested. SOFTWARE DESIGN METRICS FOR OBJECT-ORIENTED SOFTWARE 122 J OURNAL OF OBJECT TECHNOLOGY V OL. 6, NO. 1
1.Prof. and vice-chancellor, GGSIndraprastha University, Delhi, India. 2.Prof. and Dean, School O... more 1.Prof. and vice-chancellor, GGSIndraprastha University, Delhi, India. 2.Prof. and Dean, School Of Information Technolgy, GGSIndraprastha University, Delhi, India. 3.Lecturer, School Of Information Technolgy, GGSIndraprastha University, Delhi, India. 4.Department of computer science ...
Software project planning and estimation is the most important confront for software developers a... more Software project planning and estimation is the most important confront for software developers and researchers. It incorporates estimating the size of the software project to be produced, estimating the effort required, developing initial project schedules, and ultimately, estimating on the whole cost of the project. Numerous empirical explorations have been performed on the existing methods, but they lack convergence in choosing the best prediction methodology. Analogy based estimation is still one of the most extensively used method in industry which is based on finding effort from similar projects from the project repository. Two alternative approaches using analogy for estimation have been proposed in this study. Firstly, a precise and comprehensible predictive model based on the integration of Grey Relational Analysis (GRA) and regression has been discussed. Second approach deals with the uncertainty in the software projects, and how fuzzy set theory in fusion with grey relati...
Journal of Statistics and Management Systems, 2011
There are available metrics for predicting fault prone classes, which may help software organizat... more There are available metrics for predicting fault prone classes, which may help software organizations for planning and performing testing activities. This may be possible due to proper allocation of resources on fault prone parts of the design and code of the software. Hence, importance and usefulness of such metrics is understandable, but empirical validation of these metrics is always a great challenge. J48 decision tree algorithm has been successfully applied for solving classi cation problems in many applications. This paper evaluates the capability of algorithm and compares its performance with nine statistical and machine learning methods in predicting fault prone software classes using publicly available NASA data set. The results indicate that the prediction performance of J48 is generally better than other statistical and machine learning models. However, similar types of studies are required to be carried out in order to establish the acceptability of the J48 model.
The aim of this paper is to rank the impact of Object Oriented(OO) metrics in fault prediction mo... more The aim of this paper is to rank the impact of Object Oriented(OO) metrics in fault prediction modeling using Artificial Neural Networks(ANNs). Past studies on empirical validation of object oriented metrics as fault predictors using ANNs have focused on the predictive quality of neural networks versus standard statistical techniques. In this empirical study we turn our attention to the capability of ANNs in ranking the impact of these explanatory metrics on fault proneness. In ANNs data analysis approach, there is no clear method of ranking the impact of individual metrics. Five ANN based techniques are studied which rank object oriented metrics in predicting fault proneness of classes. These techniques are i) overall connection weights method ii) Garson's method iii) The partial derivatives methods iv) The Input Perturb method v) the classical stepwise methods. We develop and evaluate different prediction models based on the ranking of the metrics by the individual techniques. The models based on overall connection weights and partial derivatives methods have been found to be most accurate.
Empirical validation of software metrics to predict quality using machine learning methods is imp... more Empirical validation of software metrics to predict quality using machine learning methods is important to ensure their practical relevance in the software organizations. In this paper, we build a Support Vector Machine (SVM) model to find the relationship between object-oriented metrics given by Chidamber and Kemerer and fault proneness. The proposed model is empirically evaluated using public domain KC1 NASA data set. The performance of the SVM method was evaluated by Receiver Operating Characteristic (ROC) analysis. Based on these results, it is reasonable to claim that such models could help for planning and performing testing by focusing resources on fault-prone parts of the design and code. Thus, the study shows that SVM method may also be used in constructing software quality models.
The importance of software measurement is increasing leading to development of new measurement te... more The importance of software measurement is increasing leading to development of new measurement techniques. As the development of object-oriented software is rising, more and more metrics are being defined for object-oriented languages. Many metrics have been proposed related to various object-oriented constructs like class, coupling, cohesion, inheritance, information hiding and polymorphism. The applicability of metrics developed by previous researchers is mostly limited to requirement, design and implementation phase. Exception handling is a desirable feature of software that leads to robust design and must be measured. This research addresses this need and introduces a new set of design metrics for object-oriented code. Two metrics are developed that measure the amount of robustness included in the code. The metrics are analytically evaluated against Weyuker's proposed set of nine axioms. These set of metrics are calculated and analyzed for standard projects and accordingly ways in which project managers can utilize these metrics are suggested. SOFTWARE DESIGN METRICS FOR OBJECT-ORIENTED SOFTWARE 122 J OURNAL OF OBJECT TECHNOLOGY V OL. 6, NO. 1
1.Prof. and vice-chancellor, GGSIndraprastha University, Delhi, India. 2.Prof. and Dean, School O... more 1.Prof. and vice-chancellor, GGSIndraprastha University, Delhi, India. 2.Prof. and Dean, School Of Information Technolgy, GGSIndraprastha University, Delhi, India. 3.Lecturer, School Of Information Technolgy, GGSIndraprastha University, Delhi, India. 4.Department of computer science ...
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