JIT-CF: Integrating contrastive learning with feature fusion for enhanced just-in-time defect prediction

Context:

Just-in-time defect prediction (JIT-DP) is a crucial process in software development that focuses on identifying potential defects during code changes, facilitating early mitigation and quality assurance. Pre-trained language models like CodeBERT have shown promise in various applications but often struggle to distinguish between defective and non-defective code, especially when dealing with noisy labels.

Objective:

The primary aim of this study is to enhance the robustness of pre-trained language models in identifying software defects by developing an innovative framework that leverages contrastive learning and feature fusion.

Method:

We introduce JIT-CF, a framework that improves model robustness by employing contrastive learning to maximize similarity within positive pairs and minimize it between negative pairs, thereby enhancing the model’s ability to detect subtle differences in code changes. Additionally, feature fusion is used to combine semantic and expert features, enabling the model to capture richer contextual information. This integrated approach aims to improve the identification and resolution of code defects.

Results:

JIT-CF was evaluated using the JIT-Defects4J dataset, which includes 23,379 code commits from 21 projects. The results indicate substantial performance improvements over seven state-of-the-art baselines, with enhancements of up to 13.9% in F1-score, 8% in AUC, and 11% in Recall@20%E. The study also explores the impact of specific customization enhancements, demonstrating the potential for improved just-in-time defect localization.

Conclusion:

The proposed JIT-CF framework significantly advances the field of just-in-time defect prediction by effectively addressing the challenges encountered by pre-trained models in distinguishing code defects. The integration of contrastive learning and feature fusion not only enhances the model’s robustness but also leads to notable improvements in prediction accuracy, offering valuable insights for future applications in software development.