An empirical study on optimal solutions selection strategies for effort-aware just-in-time software defect prediction

Abstract

Just-in-time software defect prediction (JIT-SDP) is an active topic in the filed of software engineering, and many methods have been proposed to solve this problem. State-of-the-art method MULTI applies multi-objective optimization algorithm to the effort-aware JIT-SDP problem, and obtains good average performance. Although the average performance of the MULTI method is high, there are many optimal solutions with poor performance. If an optimal solution is randomly selected, a poor prediction model may be obtained. In order to further improve the performance of the MULTI method, we propose three optimal solutions selection strategies: benefit priority (BP), cost priority (CP), and a compromise between cost and benefit (CCB). In order to compare and validate the effectiveness of the strategies, we conduct a large-scale empirical study on data sets of six open source projects. The experimental results show that, compared with the average performance of MULTI, the optimal solutions selection strategy based on BP has a significant improvement in ACC and Popt indicators. Therefore, we recommend using the BP-based optimal solutions selection strategy to improve the performance of MULTI when using the MULTI method to solve the effort-aware JIT-SDP problem.