TransplantFix: Graph differencing-based code transplantation for automated program repair

• Main Authors: YANG, Deheng, MAO, Xiaoguang, CHEN, Liqian, XU, Xuezheng, LEI, Yan, LO, David, HE, Jiayu
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Automated program repair; code transplantation; graph differencing; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/7731; https://ink.library.smu.edu.sg/context/sis_research/article/8734/viewcontent/ASE22_TFix_pv.pdf
• Institution: Singapore Management University

Automated program repair (APR) holds the promise of aiding manual debugging activities. Over a decade of evolution, a broad range of APR techniques have been proposed and evaluated on a set of real-world bug datasets. However, while more and more bugs have been correctly fixed, we observe that the growth of newly fixed bugs by APR techniques has hit a bottleneck in recent years. In this work, we explore the possibility of addressing complicated bugs by proposing TransplantFix, a novel APR technique that leverages graph differencing-based transplantation from the donor method. The key novelty of TransplantFix lies in three aspects: 1) we propose to use a graph-based differencing algorithm to distill semantic fix actions from the donor method; 2) we devise an inheritance-hierarchy-aware code search approach to identify donor methods with similar functionality; 3) we present a namespace transfer approach to effectively adapt donor code. We investigate the unique contributions of TransplantFix by conducting an extensive comparison that covers a total of 42 APR techniques and evaluating TransplantFix on 839 real-world bugs from Defects4J v1.2 and v2.0. TransplantFix presents superior results in three aspects. First, it has achieved the best performance as compared to the state-of-the-art APR techniques proposed in the last three years, in terms of the number of newly fixed bugs, reaching a 60%-300% improvement. Furthermore, not relying on any fix actions crafted manually or learned from big data, it reaches the best generalizability among all APR techniques evaluated on Defects4J v1.2 and v2.0. In addition, it shows the potential to synthesize complicated patches consisting of at most eight-line insertions at a hunk. TransplantFix presents fresh insights and a promising avenue for follow-up research towards addressing more complicated bugs.