A code change-oriented approach to just-in-time defect prediction with multiple input semantic fusion

Recent research found that fine-tuning pre-trained models is superior to training models from scratch in just-in-time (JIT) defect prediction. However, existing approaches using pre-trained models have their limitations. First, the input length is constrained by the pre-trained models.Secondly, the inputs are change-agnostic.To address these limitations, we propose JIT-Block, a JIT defect prediction method that combines multiple input semantics using changed block as the fundamental unit. We restructure the JIT-Defects4J dataset used in previous research. We then conducted a comprehensive comparison using eleven performance metrics, including both effort-aware and effort-agnostic measures, against six state-of-the-art baseline models. The results demonstrate that on the JIT defect prediction task, our approach outperforms the baseline models in all six metrics, showing improvements ranging from 1.5% to 800% in effort-agnostic metrics and 0.3% to 57% in effort-aware metrics. For the JIT defect code line localization task, our approach outperforms the baseline models in three out of five metrics, showing improvements of 11% to 140%.