文献类型：期刊文献

英文题名：JIT-Align: A Semantic Alignment-Based Ranking Framework for Just-In-Time Defect Prediction

作者：Ye, Yujie[1]; Yu, Huiqun[1,2]; Fan, Guisheng[1,2]; Liang, Yuguo[1]; Dong, Jianan[1]; Chen, Wentao[1]

机构：[1] East China University of Science and Technology, Department of Computer Science and Engineering, Shanghai, 200237, China; [2] Shanghai Engineering Research Center of Smart Energy, Shanghai, 201103, China

年份：2025

起止页码：1328

外文期刊名：Proceedings - 2025 IEEE 49th Annual Computers, Software, and Applications Conference, COMPSAC 2025

收录：EI(收录号：20253819196731)

语种：英文

外文关键词：Codes (symbols) - Defects - Forecasting - Large datasets - Prediction models - Semantics

摘要：To promptly identify software defects and prevent defective code changes from being integrated into the repository, Just-In-Time Software Defect Prediction (JIT-SDP) has demonstrated promising research findings. Recent studies have begun to utilize Pre-trained Models (PTMs) for training and prediction, yet these models inherently impose input length limitations, leading to forced truncation of inputs. However, previous work has largely overlooked the impact of forced truncation, even though it may inadvertently discard critical input information, leading to degraded model performance. Moreover, some existing methods fail to maintain consistency in truncation during each model construction process, leading to unexplainable truncations and unstable model performance. In addition, previous datasets suffer from limitations and incompleteness. To this end, we construct a large-scale and comprehensive dataset, MC4Defect. Moreover, we propose JIT-Align, which prioritizes code changes within a commit using a semantic alignment algorithm to make full use of the limited input space of PTMs. To evaluate the feasibility of JIT-Align, we first assess the classification capability of our method by comparing it against four baselines across five datasets. Then, we conduct ablation studies on the proposed semantic alignment framework to validate its effectiveness. Experimental results show that JIT-Align, along with its semantic alignment framework, outperforms all baselines in JIT-SDP tasks, with average F1 score improvements of 3.1%-9.6% and MCC increases of 3.1%-9.7% across all projects, exhibiting higher stability and better interpretability compared to alternative approaches. ? 2025 IEEE.