RESEARCH NOTES — GMRepair: Graph Mining Template-Based Automated Software Repair

With the increasing scale and complexity of software recently, automated software bug repair has grown in importance. However, the current automated software bug repair process suffers from issues such as coarse-grained repair granularity and poor patch quality. To address these problems, we propose a graph mining template-based automatic software repair (GMRepair) to improve the performance of automated software bug repair. First, this approach adopts the Ochiai fault localization technique to locate and generate a list of suspicious defect statements. We utilize the GumTree tool to parse the bug and repair program files, generating edit scripts. These edit scripts are then transformed into a graphical representation. Second, we utilize a frequent graph miner to obtain graph mining templates by matching the context of the suspicious statements with the context of the graph mining templates, generating an initial population for them. The buggy program is evolved using genetic programming through mutation and crossover operations, generating new individuals. Finally, we sequentially pass the candidate patches (CPs) through corresponding test cases and prioritize the test cases using priority sorting techniques. Patches that fail to pass the test cases are filtered out, and the patches that pass the test cases are output. We conducted the experiments using two datasets, QuixBugs and Defects4J. In Defects4J, the GMRepair successfully repaired 41 defects, while in QuixBugs, it successfully repaired 15 defects. Compared to the existing methods, GMRepair offers a higher success rate and efficiency in defect repair.