Enhancing Feature Quality in JIT-SDP: An Empirical Study of Branch Dependency Mining

Just-in-time software defect prediction (JIT-SDP) aims to predict defect-inducing software changes in a timely manner, thereby enhancing development efficiency and product quality, especially during the software maintenance phase. Previous studies have primarily focused on proposing novel methods to achieve stronger predictive performance, often evaluated using open-source projects produced through data extraction tools. This extraction process typically operates under the implicit assumption that the extracted data features are exempt from noise. However, limited attention has been given to data quality, particularly regarding feature quality. We have identified that common feature extraction methods, such as those implemented by verb—Commit Guru—usually overlook branch dependency information, which can compromise feature quality and lead to unreliable or invalid conclusions. This paper systematically evaluates the quality of features extracted in JIT-SDP and investigates their impact on predictive performance. Moreover, we propose a novel feature extraction method that accounts for branch dependency to produce high-quality features. Our experiments based on 22 open projects reveal that neglecting branch dependency can significantly affect feature quality; however, this issue has a limited impact on the predictive performance of JIT-SDP models. Our results reveal that, although neglecting branch dependency introduces measurable feature discrepancies, the downstream predictive performance and model rankings of JIT-SDP models remain largely stable. These findings provide strong evidence regarding the empirical robustness of prior JIT-SDP studies, suggesting that the field's foundational conclusions are not compromised by this specific data quality issue. Our enhanced feature extraction method is publicly available at https://github.com/HongJinSecond/Feature-study.