An Innovative Method for Efficient Coverage Data Reduction in Multiple Fault Localization

In software debugging, fault localization (FL) is an essential stage that is used to identify accurate location of faulty statement. It is well known that coverage data plays an important role in FL. In past studies, traditional principal component analysis (PCA) or revised PCA techniques were used to reduce coverage data. However, two kinds of PCA have a great opportunity to remove the actual faulty statement, especially in multiple fault localization. On the other hand, they cannot reflect the status of the statements. In this paper, we propose a novel approach based on revised PCA and incorporate the result of failed and passed test case different combinations to update contribution value of each statement. We used two Linux open-source codes (Sed, Grep) with 4 fault injections to verify the correctness. Preliminary experiments have shown that our proposed method is feasible, scalable, and shorter execution time of FL process, and also can alleviate the situations for removed faulty statements compared to the revised PCA method.