Mixed Granularity and Variable Mapping based Automatic Software Repair
Heling Cao, Zhiying Cui, Yangxia Meng, Yonghe Chu, Lei Li · 2021 IEEE 21st International Conference on Software Quality, Reliability and Security Companion (QRS-C) · 2021
During the process of software repair, the efficiency of the repair is lower because the granularity of repair is too coarse and the way of fixing ingredient is too simple. To resolve the problems, we propose a Mixed granularity and Variable mapping based automatic software Repair (MVRepair). We adopt random search algorithm as the framework of program evolution, and utilize the mapping relationship between variables as an auxiliary specification. Firstly, fault localization is used to locate the suspicious statements and to form a list of modification points. Secondly, the ingredient of program repair at statement level is obtained, and the mapping relationship of variables is established. Then, the test case prioritization is improved from the perspective of the modification point. Finally, a program passes all test cases or the program iteration terminates. The experimental results show that MVRepair has a higher repair success rate than GenProg, CapGen, SimFix, jKali, and jMutRepair on Defects4J.