| Description |
1 online resource |
| Series |
SpringerBriefs in Computer Science |
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SpringerBriefs in computer science.
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| Contents |
Intro; Preface; Acknowledgements; Contents; About the Authors; 1 Introduction; 1.1 Software Fault Prediction; 1.2 Classification of Software Faults; 1.3 Advantages of Software Fault Prediction; 1.4 Organization of the Book; 1.5 Summary; References; 2 Techniques Used for the Prediction of Number of Faults; 2.1 Regression Techniques; 2.1.1 Linear Regression; 2.1.2 Logistic Regression; 2.1.3 Ridge Regression; 2.1.4 Polynomial Regression; 2.1.5 Principal Component Regression; 2.1.6 Poisson Regression; 2.1.7 Negative Binomial Regression; 2.1.8 Partial Least Square Regression |
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2.1.9 Ordinal Regression2.1.10 Quasi-Poisson Regression; 2.1.11 Lasso Regression; 2.1.12 Support Vector Regression; 2.2 Ensemble Methods for Regression; 2.2.1 Homogeneous Ensemble Methods; 2.2.2 Heterogeneous Ensemble Methods; 2.3 State-of-Art of Regression and Ensemble Methods for the Prediction of Number of Faults; 2.4 Performance Evaluation Measures; 2.5 Summary; References; 3 Homogeneous Ensemble Methods for the Prediction of Number of Faults; 3.1 Homogeneous Ensemble Methods; 3.2 Evaluation of Homogeneous Ensemble Methods; 3.2.1 Software Fault Datasets; 3.2.2 Experimental Setup |
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3.2.3 Experimental Procedure3.2.4 Tools and Techniques Used for Experiment; 3.3 Results and Discussion; 3.3.1 Results of Group-1 for MAE and MRE; 3.3.2 Results of Group-2 for MAE and MRE; 3.3.3 Results of Group-3 for MAE and MRE; 3.3.4 Results of Group-4 for MAE and MRE; 3.3.5 Results of Pred(0.30) for All Groups of Fault Datasets; 3.4 Summary; References; 4 Linear Rule Based Ensemble Methods for the Prediction of Number of Faults; 4.1 Linear Rule Based Heterogeneous Ensemble Methods; 4.2 Evaluation of Linear Rule Based Heterogeneous Ensemble Methods; 4.2.1 Software Fault Datasets |
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4.2.2 Experimental Setup4.2.3 Experimental Procedure; 4.2.4 Tools and Techniques Used for Experiment; 4.3 Results and Discussion; 4.3.1 Results of Group-1 for MAE and MRE; 4.3.2 Results of Group-2 for MAE and MRE; 4.3.3 Results of Group-3 for MAE and MRE; 4.3.4 Results of Group-4 for MAE and MRE; 4.3.5 Results of Pred(0.30) for All Groups of Fault Datasets; 4.4 Summary; References; 5 Nonlinear Rule Based Ensemble Methods for the Prediction of Number of Faults; 5.1 Nonlinear Rule Based Heterogeneous Ensemble Methods; 5.2 Evaluation of Nonlinear Rule Based Heterogeneous Ensemble Methods |
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5.2.1 Software Fault Datasets5.2.2 Experimental Setup; 5.2.3 Experimental Procedure; 5.2.4 Nonlinear Learning Technique; 5.2.5 Tools and Techniques Used for Experiment; 5.3 Results and Discussion; 5.3.1 Results of Group-1 for MAE and MRE; 5.3.2 Results of Group-2 for MAE and MRE; 5.3.3 Results of Group-3 for MAE and MRE; 5.3.4 Results of Group-4 for MAE and MRE; 5.3.5 Results of Pred(0.30) for All Groups of Fault Datasets; 5.4 Summary; References; 6 Conclusions; Closing Remarks; Index |
| Summary |
This book addresses software faults--a critical issue that not only reduces the quality of software, but also increases their development costs. Various models for predicting the fault-proneness of software systems have been proposed; however, most of them provide inadequate information, limiting their effectiveness. This book focuses on the prediction of number of faults in software modules, and provides readers with essential insights into the generalized architecture, different techniques, and state-of-the art literature. In addition, it covers various software fault datasets and issues that crop up when predicting number of faults. A must-read for readers seeking a "one-stop" source of information on software fault prediction and recent research trends, the book will especially benefit those interested in pursuing research in this area. At the same time, it will provide experienced researchers with a valuable summary of the latest developments. -- Provided by publisher |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Online resource; title from PDF title page (EBSCO, April 5, 2019) |
| Subject |
Computer software -- Reliability
|
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Computer software -- Testing
|
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Computer software -- Evaluation
|
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Information technology industries.
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Software Engineering.
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COMPUTERS -- Software Development & Engineering -- Quality Assurance & Testing.
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Computer software -- Evaluation
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Computer software -- Reliability
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Computer software -- Testing
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| Form |
Electronic book
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| Author |
Kumar, Sandeep
|
| ISBN |
9789811371318 |
|
9811371318 |
|
9789811371325 |
|
9811371326 |
|
9789811371332 |
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9811371334 |
|
