JIT-Smart: A Multi-task Learning Framework for Just-in-Time Defect Prediction and Localization

Abstract

Just-in-time defect prediction (JIT-DP) is used to predict the defect-proneness of a commit and just-in-time defect localization (JIT-DL) is used to locate the exact buggy positions (defective lines) in a commit. Recently, various JIT-DP and JIT-DL techniques have been proposed, while most of them use a post-mortem way (e.g., code entropy, attention weight, LIME) to achieve the JIT-DL goal based on the prediction results in JIT-DP. These methods do not utilize the label information of the defective code lines during model building. In this paper, we propose a unified model JIT-Smart, which makes the training process of just-in-time defect prediction and localization tasks a mutually reinforcing multi-task learning process. Specifically, we design a novel defect localization network (DLN), which explicitly introduces the label information of defective code lines for supervised learning in JIT-DL with considering the class imbalance issue. To further investigate the accuracy and cost-effectiveness of JIT-Smart, we compare JIT-Smart with 7 state-of-the-art baselines under 5 commit-level and 5 line-level evaluation metrics in JIT-DP and JIT-DL. The results demonstrate that JIT-Smart is statistically better than all the state-of-the-art baselines in JIT-DP and JIT-DL. In JIT-DP, at the median value, JIT-Smart achieves F1-Score of 0.475 , AUC of 0.886 , Recall@20%Effort of 0.823 , Effort@20%Recall of 0.01 and Popt of 0.942 and improves the baselines by 19 .89% - 702 .74% , 1 .23% - 31 .34% , 9 .44% - 33 .16% , 21 .6% - 53 .82% and 1 .94% - 34 .89% , respectively. In JIT-DL, at the median value, JIT-Smart achieves Top-5 Accuracy of 0.539 and Top-10 Accuracy of 0.396 , Recall@ 20 % Effort l i n e of 0.726 , Effort@ 20 % Recall l i n e of 0.087 and IFA l i n e