关键词: 软件缺陷预测, 基于搜索的软件工程, 特征选择, 多目标优化

Abstract: Software defect prediction can identify potential defective modules in advance. It provides a guidance for software testers to allocate more testing resources on these modules for improving software quality. During the gathering process for defect prediction datasets, if multiple metrics (i.e., features) are used to measure the program modules, it will result in the curse of dimensionality. Feature selection is one of effective methods to alleviate this problem. It aims to identify and remove redundant and irrelevant features as many as possible. However, designing effective feature selection methods is a challenge problem. This paper formulizes the problem as a multi-objective optimization problem. One objective is to minimize the number of selected features. Another objective is to maximize the performance of trained model. Then, this paper proposes a novel method MOFES (multi-objective optimization feature selection) to find a balance between these two conflict objectives. To verify the effectiveness of the proposed method, this paper chooses PROMISE and RELINK datasets gathered from real open source projects, and compares MOFES with some classical baseline methods, such as GFS, GBS and SOFS. Final results show that the proposed method has the advantages of selecting fewer features and achieving better prediction performance in most projects while its computational cost is acceptable.

Key words: software defect prediction, search based software engineering, feature selection, multi-objective optimization