The impact of changes mislabeled by SZZ on just-in-time defect prediction

• Main Authors: FAN, Yuanrui, XIA, Xin, COSTA, Daniel A., LO, David, HASSAN, Ahmed E., LI, Shanping
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: Just-in-Time Defect Prediction; SZZ; Noisy Data; Mining Software Repositories; Data Storage Systems; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/4494; https://ink.library.smu.edu.sg/context/sis_research/article/5497/viewcontent/tse194.pdf
• Institution: Singapore Management University

Just-in-Time (JIT) defect prediction—a technique which aims to predict bugs at change level—has been paid more attention. JIT defect prediction leverages the SZZ approach to identify bug-introducing changes. Recently, researchers found that the performance of SZZ (including its variants) is impacted...