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

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.