3PcGE: 3-parent child-based genetic evolution for software defect prediction

Software defect prediction (SDP) is the most fascinating research area in software industry to enhance the quality of software products. SDP classifiers predict the fault-prone modules in early development phases prior to begin testing phase, and thence, the testing efforts can be focused to those predicted fault-prone modules. In this way, the early detection of fault-prone modules increases the chances to release error-free products to the clients with reduced testing efforts and cost. For SDP application, which uses voluminous high-dimensional data, feature selection (FS) has become essential data preprocessing technique. From past three decades, search-based feature selection is prominently deployed to improve the efficiency of predictors. This paper proposes a new approach, namely 3PcGE, for feature selection (FS) based on three-parent child (3Pc) and genetic evolution (GE). The 3PcGE is inspired by evolutionary computation involving three-parent biological evolution process to result an off-spring with best survival capability. The 3Pc separates the spindle from the mother’s cell body having defective mitochondria and replaces the separated spindle in the emptied donor cell body having healthy mitochondria. In this way, 3-parent child is healthier than 2-parent child and free from fatal disease. 3PcGE searches the feature space for an optimal feature subset using the performance of classification and number of features selected as fitness function. The FS is modeled as multi-objective optimization problem, and pareto optimal solution is sought using evolutionary algorithm (3PcGE). The performance is compared with the state-of-the-art FS technique. From experimental results, it is clear that the proposed 3PcGE outperforms the competing filter-based FS techniques by 18.98% and wrapper-based FS techniques by 17.5% in AUC measure. The statistical comparison with the baseline technique (NSGA-II) shows that proposed FS technique 3PcGE is effective to select optimal features and results in better accuracy of SDP models.