一句话：提出统一模型 JIT-Fine，融合语义与专家特征，联合进行即时缺陷预测和定位，并构建行级数据集。
即时缺陷预测（JIT-DP）与即时缺陷定位（JIT-DL）分别利用语义特征或专家特征取得进展，但二者尚未充分结合，且 JIT-DL 通常依赖 JIT-DP，难以在提交内精确定位缺陷行。本文提出统一模型 JIT-Fine，融合语义特征与专家特征，联合完成提交级缺陷预测与行级缺陷定位，并构建大规模行级人工标注数据集 JIT-Defects4J。在与六种先进基线的全面比较中，采用十种性能度量（分为不考虑工作量与考虑工作量两类），JIT-Fine 在 JIT-DP 和 JIT-DL 上均优于所有基线：JIT-DP 的不考虑工作量指标提升 10%-629%、考虑工作量指标提升 5%-54%，JIT-DL 的考虑工作量指标提升 4%-117%。
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).
@inproceedings{paperbot7005,
title = {The best of both worlds: integrating semantic features with expert features for defect prediction and localization},
author = {Chao Ni and Wei Wang and Kaiwen Yang and Xin Xia and Kui Liu and David Lo},
booktitle = {FSE},
year = {2022},
doi = {10.1145/3540250.3549165}
}