Bug severity classification in software using ant colony optimization based feature weighting technique

At the present, the delivery of the software should be time-bound without affecting the quality of the software. However, bug severity can affect the timely delivery of software. It is a crucial component of the software engineering, including maintenance and testing. Both phases are essential for bug severity classification but require much time. Generally, bug triage is responsible for classifying the bugs based on criticality/severeness. The manual execution of this process is error-prone. Consequently, a model for automatic bug classification is required to help the bug triage. In this work, the ant colony optimization (ACO) based feature extraction technique is proposed to extract more relevant features for bug severity classification. Furthermore, the ACO technique is integrated with NB, SVM, DeepFM and F-SVM techniques for predicting bug severity and classifying bugs into multi-severity classes. Several benchmark projects such as Eclipse, Mozilla, OpenFOAM, JBoss, and Firefox, are considered to evaluate the efficacy of the techniques above. The simulation outcomes are expressed in terms of Accuracy, Precision, Recall, and F1-measure. It is noted that the outcomes of the SVM, NB, DeepFM and F-SVM approaches are improved by the ACO-based feature weighting technique. The accuracy rate of ACO-FSVM, ACO-NB, ACO-SVM, ACO-DeepFM, NB, SVM, F-SVM, DeepFM techniques are ranging in between 85.73 and 89.38%, 78% to 80%, 73% to 76%, 92.67% to 97.27 %, 71% to 77%,65% to 74%, 78.21% to 81.28% and 90.02% to 95.24% respectively for five benchmark projects. Further, proposed techniques are also produced better simulation results as compared with state-of -the-art techniques. Friedman and post hoc statistical tests are also conducted on proposed techniques.