Software Defect Prediction Based on the Value of an Acceptable Risk Level Method

The knowledge about the software metrics, which serve as quality indicators, is vital for the efficient allocation of resources in quality assurance activities. Recent studies showed that some software metrics exhibit threshold effects and can be used for software defect prediction. Our goal was to analyze if the threshold derivation process could be used to improve a standard classification models for software defect prediction, rather than to search for universal threshold values. We proposed two classification models based on Bender method for threshold derivation to test this idea, named Threshold Naive Bayes and Threshold Voting. Threshold Naive Bayes is a probabilistic classifier based on Naive Bayes and improved by threshold derivation. Threshold Voting is a simple type of ensemble classifier which is based solely on threshold derivation. The proposed models were tested in a case study based on datasets from subsequent releases of large open source projects and compared against the standard Naive Bayes classifier in terms of geometric mean (GM) between true positive and true negative rate. The results of our case study showed that the Threshold Naive Bayes classifier performs better than the other two when compared in terms of GM. Hence, this study has shown that threshold derivation process for software metrics may be used to improve the performance of standard classifiers in software defect prediction. Future research will analyze its effectiveness in general classification purposes and test on other types of data.The knowledge about the software metrics, which serve as quality indicators, is vital for the efficient allocation of resources in quality assurance activities. Recent studies showed that some software metrics exhibit threshold effects and can be used for software defect prediction. Our goal was to analyze if the threshold derivation process could be used to improve a standard classification models for software defect prediction, rather than to search for universal threshold values. We proposed two classification models based on Bender method for threshold derivation to test this idea, named Threshold Naive Bayes and Threshold Voting. Threshold Naive Bayes is a probabilistic classifier based on Naive Bayes and improved by threshold derivation. Threshold Voting is a simple type of ensemble classifier which is based solely on threshold derivation. The proposed models were tested in a case study based on datasets from subsequent releases of large open source projects and compared against the standard Naive Bayes classifier in terms of geometric mean (GM) between true positive and true negative rate. The results of our case study showed that the Threshold Naive Bayes classifier performs better than the other two when compared in terms of GM. Hence, this study has shown that threshold derivation process for software metrics may be used to improve the performance of standard classifiers in software defect prediction. Future research will analyze its effectiveness in general classification purposes and test on other types of data