The best of both worlds: integrating semantic features with expert features for defect prediction and localization

• Main Authors: NI, Chao, WANG, Wei, YANG, Kaiwen, XIA, Xin, LIU, Kui, LO, David
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/7729
• Institution: Singapore Management University

To improve software quality, just-in-time defect prediction (JIT-DP) (identifying defect-inducing commits) and just-in-time defect localization (JIT-DL) (identifying defect-inducing code lines in commits) have been widely studied by learning semantic features or expert features respectively, and indeed achieved promising performance. Semantic features and expert features describe code change commits from different aspects, however, the best of the two features have not been fully explored together to boost the just-in-time defect prediction and localization in the literature yet. Additional, JIT-DP identifies defects at the coarse commit level, while as the consequent task of JIT-DP, JIT-DL cannot achieve the accurate localization of defect-inducing code lines in a commit without JIT-DP. We hypothesize that the two JIT tasks can be combined together to boost the accurate prediction and localization of defect-inducing commits by integrating semantic features with expert features. Therefore, we propose to build a unified model, JIT-Fine, for the just-in-time defect prediction and localization by leveraging the best of semantic features and expert features. To assess the feasibility of JIT-Fine, we first build a large-scale line-level manually labeled dataset, JIT-Defects4J. Then, we make a comprehensive comparison with six state-of-the-art baselines under various settings using ten performance measures grouped into two types: effort-agnostic and effort-aware. The experimental results indicate that JIT-Fine can outperform all state-of-the-art baselines on both JIT-DP and JITDL tasks in terms of ten performance measures with a substantial improvement (i.e., 10%-629% in terms of effort-agnostic measures on JIT-DP, 5%-54% in terms of effort-aware measures on JIT-DP, and 4%-117% in terms of effort-aware measures on JIT-DL).