CG-FL: A data augmentation approach using context-aware genetic algorithm for fault localization

Abstract

Fault localization (FL) is a critical step in software debugging. Coverage-based fault localization (CFL) as one of the most promising FL technique utilizes coverage information obtained from program entities executed by test cases to determine the entities that are more likely to be faulty. However, CFL faces two main issues that limit its effectiveness. Firstly, the code coverage data contains numerous irrelevant statements for the observed failure, which makes the search scope too large for FL. Secondly, the input coverage data is highly imbalanced due to the presence of significantly more passing test cases than failing test cases, which makes the FL model bias to the passing test cases. To address these problems, we propose CG-FL, a data augmentation approach using context-aware genetic algorithm. Specifically, CG-FL first uses program slicing to construct a failure context for FL. Subsequently, CG-FL generate synthesized failing test cases through the application of the genetic algorithm. To evaluate the effectiveness of CG-FL, we compared it with six state-of-the-art FL methods and three representative data augmentation methods on 420 versions of 9 benchmarks. The experimental findings clearly indicate that CG-FL substantially enhances the effectiveness of the six FL methods and outperforms the three data augmentation methods.