Mitigating regression faults induced by feature evolution in deep learning systems

• Main Authors: YU, Hanmo, WANG, Zan, CHEN, Xuyang, CHEN, Junjie, SUN, Jun, LIU, Shuang, DONG, Zishuo
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2025
• Subjects: Regression Mitigation; Regression Fault; Deep Learning; Feature Evolution; Fault Mitigation; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/10116; https://ink.library.smu.edu.sg/context/sis_research/article/11116/viewcontent/MigatingRegressionsFaults_pvoa.pdf
• Institution: Singapore Management University

Deep learning (DL) systems have been widely utilized across various domains. However, the evolution of DL systems can result in regression faults. In addition to the evolution of DL systems through the incorporation of new data, feature evolution, such as the addition of new features, is also common and can introduce regression faults. In this work, we first investigate the underlying factors that are correlated with regression faults in feature evolution scenarios, i.e., redundancy and contribution shift. Based on our investigation, we propose a novel mitigation approach called FeaProtect, which aims to minimize the impact of these two factors. To evaluate the performance of FeaProtect, we conducted an extensive study comparing it with state-of-the-art approaches. The results show that FeaProtect outperforms the in-processing baseline approaches, with an average improvement of 50.6% ∼ 56.4% in terms of regression fault mitigation. We also show that FeaProtect can further enhance the effectiveness of mitigating regression faults by integrating with state-of-the-art post-processing approaches.