ICLNet: Stepping Beyond Dates for Robust Issue-Commit Link Recovery

In the field of software engineering, effectively managing software systems is essential. A key aspect of this management is the issue-commit link, which connects reported problems or enhancement requests (issues) with the actual code changes implemented in the software (commits). However, the robustness of various automated link recovery techniques, including the leading ML based model Hybrid Linker remains a subject of discussion. In this study, we investigate the Hybrid Linker model using interpretability tools like LIME and SHAP to understand its decision-making, especially its reliance on specific features. We assess its robustness against adversarial attacks, revealing its sensitivity to non-textual features like issue and commit dates. To address this, we introduce ICLNet (Issue Commit Link Network), which leverages BERT embeddings in a custom neural network. Our extensive adversarial tests show that ICLNet outperforms Hybrid Linker in adversarial settings, demonstrating greater resilience. ICLNet achieves a remarkable average F-score of 88.39% in adversarial scenarios, significantly surpassing Hybrid Linker's 62.11%. This confirms ICLNet's superiority in diverse conditions, highlighting its accuracy and robustness.