Prediction of Defective Software Modules Using Class Imbalance Learning
Software defect predictors are useful to maintain the high quality of software products effectively. The early prediction of defective software modules can help the software developers to allocate the available resources to deliver high quality software products. The objective of software defect pre...
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2016/7658207 |
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| author | Divya Tomar Sonali Agarwal |
| author_facet | Divya Tomar Sonali Agarwal |
| author_sort | Divya Tomar |
| collection | DOAJ |
| description | Software defect predictors are useful to maintain the high quality of software products effectively. The early prediction of defective software modules can help the software developers to allocate the available resources to deliver high quality software products. The objective of software defect prediction system is to find as many defective software modules as possible without affecting the overall performance. The learning process of a software defect predictor is difficult due to the imbalanced distribution of software modules between defective and nondefective classes. Misclassification cost of defective software modules generally incurs much higher cost than the misclassification of nondefective one. Therefore, on considering the misclassification cost issue, we have developed a software defect prediction system using Weighted Least Squares Twin Support Vector Machine (WLSTSVM). This system assigns higher misclassification cost to the data samples of defective classes and lower cost to the data samples of nondefective classes. The experiments on eight software defect prediction datasets have proved the validity of the proposed defect prediction system. The significance of the results has been tested via statistical analysis performed by using nonparametric Wilcoxon signed rank test. |
| format | Article |
| id | doaj-art-3a383b3cde5549b3968c0292f96994a1 |
| institution | Kabale University |
| issn | 1687-9724 1687-9732 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-3a383b3cde5549b3968c0292f96994a12025-02-03T06:13:32ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322016-01-01201610.1155/2016/76582077658207Prediction of Defective Software Modules Using Class Imbalance LearningDivya Tomar0Sonali Agarwal1Indian Institute of Information Technology, No. 5203, CC-3 Building, Allahabad, Uttar Pradesh 211012, IndiaIndian Institute of Information Technology, No. 5203, CC-3 Building, Allahabad, Uttar Pradesh 211012, IndiaSoftware defect predictors are useful to maintain the high quality of software products effectively. The early prediction of defective software modules can help the software developers to allocate the available resources to deliver high quality software products. The objective of software defect prediction system is to find as many defective software modules as possible without affecting the overall performance. The learning process of a software defect predictor is difficult due to the imbalanced distribution of software modules between defective and nondefective classes. Misclassification cost of defective software modules generally incurs much higher cost than the misclassification of nondefective one. Therefore, on considering the misclassification cost issue, we have developed a software defect prediction system using Weighted Least Squares Twin Support Vector Machine (WLSTSVM). This system assigns higher misclassification cost to the data samples of defective classes and lower cost to the data samples of nondefective classes. The experiments on eight software defect prediction datasets have proved the validity of the proposed defect prediction system. The significance of the results has been tested via statistical analysis performed by using nonparametric Wilcoxon signed rank test.http://dx.doi.org/10.1155/2016/7658207 |
| spellingShingle | Divya Tomar Sonali Agarwal Prediction of Defective Software Modules Using Class Imbalance Learning Applied Computational Intelligence and Soft Computing |
| title | Prediction of Defective Software Modules Using Class Imbalance Learning |
| title_full | Prediction of Defective Software Modules Using Class Imbalance Learning |
| title_fullStr | Prediction of Defective Software Modules Using Class Imbalance Learning |
| title_full_unstemmed | Prediction of Defective Software Modules Using Class Imbalance Learning |
| title_short | Prediction of Defective Software Modules Using Class Imbalance Learning |
| title_sort | prediction of defective software modules using class imbalance learning |
| url | http://dx.doi.org/10.1155/2016/7658207 |
| work_keys_str_mv | AT divyatomar predictionofdefectivesoftwaremodulesusingclassimbalancelearning AT sonaliagarwal predictionofdefectivesoftwaremodulesusingclassimbalancelearning |