Regression Identification of Coincidental Correctness via Weighted Clustering

Coverage-based fault localization techniques leverage coverage information to identify the suspicious program entities for inspection. However, coincidental correctness (CC) widely occurs during software debugging, and brings negative impact to the effectiveness of CBFL techniques. In this paper, we propose a regression approach to identity CC execution with weighted clustering analysis. Based on the observation that program entities with different suspiciousness have different contributions to identify coincidental correctness, we make use of the suspiciousness calculated by CBFL techniques as the weight of each program entity and conduct weighted clustering to identify coincidental correctness regressively. To evaluate the effectiveness of our approach, we construct controlled experiments built on benchmark programs, and the experimental results show that our approach is able to improve the accuracy of the identification of coincidental correctness executions and further improve the effectiveness of CBFL techniques.