On reliability of patch correctness assessment

• Main Authors: LE, Xuan-Bach D., BAO, Lingfeng, LO, David, XIA, Xin, LI, Shanping, PASAREANU, Corina S.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: Automated program repair; empirical study; test case generation; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/4481; https://ink.library.smu.edu.sg/context/sis_research/article/5484/viewcontent/icse192.pdf
• Institution: Singapore Management University

Current state-of-the-art automatic software repair (ASR) techniques rely heavily on incomplete specifications, or test suites, to generate repairs. This, however, may cause ASR tools to generate repairs that are incorrect and hard to generalize. To assess patch correctness, researchers have been following two methods separately: (1) Automated annotation, wherein patches are automatically labeled by an independent test suite (ITS) – a patch passing the ITS is regarded as correct or generalizable, and incorrect otherwise, (2) Author annotation, wherein authors of ASR techniques manually annotate the correctness labels of patches generated by their and competing tools. While automated annotation cannot ascertain that a patch is actually correct, author annotation is prone to subjectivity. This concern has caused an on-going debate on the appropriate ways to assess the effectiveness of numerous ASR techniques proposed recently. In this work, we propose to assess reliability of author and automated annotations on patch correctness assessment. We do this by first constructing a gold set of correctness labels for 189 randomly selected patches generated by 8 state-of-the-art ASR techniques through a user study involving 35 professional developers as independent annotators. By measuring inter-rater agreement as a proxy for annotation quality – as commonly done in the literature – we demonstrate that our constructed gold set is on par with other high-quality gold sets. We then compare labels generated by author and automated annotations with this gold set to assess reliability of the patch assessment methodologies. We subsequently report several findings and highlight implications for future studies.