Is the ground truth really accurate? Dataset purification for automated program repair

• Main Authors: YANG, Deheng, LEI, Yan, MAO, Xiaoguang, LO, David, XIE, Huan, YAN, Meng
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: bug dataset; automated program repair; dataset purification; Artificial Intelligence and Robotics; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/6878; https://ink.library.smu.edu.sg/context/sis_research/article/7881/viewcontent/963000a096.pdf
• Institution: Singapore Management University

Datasets of real-world bugs shipped with human-written patches are intensively used in the evaluation of existing automated program repair (APR) techniques, wherein the human-written patches always serve as the ground truth, for manual or automated assessment approaches, to evaluate the correctness of test-suite adequate patches. An inaccurate human-written patch tangled with other code changes will pose threats to the reliability of the assessment results. Therefore, the construction of such datasets always requires much manual effort on isolating real bug fixes from bug fixing commits. However, the manual work is time-consuming and prone to mistakes, and little has been known on whether the ground truth in such datasets is really accurate.In this paper, we propose DEPTEST, an automated DatasEt Purification technique from the perspective of triggering Tests. Leveraging coverage analysis and delta debugging, DEPTEST can automatically identify and filter out the code changes irrelevant to the bug exposed by triggering tests. To measure the strength of DEPTEST, we run it on the most extensively used dataset (i.e., Defects4J) that claims to already exclude all irrelevant code changes for each bug fix via manual purification. Our experiment indicates that even in a dataset where the bug fix is claimed to be well isolated, 41.01% of human-written patches can be further reduced by 4.3 lines on average, with the largest reduction reaching up to 53 lines. This indicates its great potential in assisting in the construction of datasets of accurate bug fixes. Furthermore, based on the purified patches, we re-dissect Defects4J and systematically revisit the APR of multi-chunk bugs to provide insights for future research targeting such bugs.