Kernel-based Detection of Coincidentally Correct Test Cases to Improve Fault Localization Effectiveness
This work addresses a specific bottleneck in software debugging for developers, though it appears incremental as it builds on existing SBFL techniques.
The paper tackled the problem of coincidental correctness (CC) in spectrum-based fault localization (SBFL), which degrades performance when passing test cases exercise faulty statements, and proposed a kernel-based SVM method to detect CC test cases, showing it effectively improves SBFL performance.
Although empirical studies have confirmed the effectiveness of spectrum-based fault localization (SBFL) techniques, their performance may be degraded due to presence of some undesired circumstances such as the existence of coincidental correctness (CC) where one or more passing test cases exercise a faulty statement and thus causing some confusion to decide whether the underlying exercised statement is faulty or not. This article aims at improving SBFL effectiveness by mitigating the effect of CC test cases. In this regard, a new method is proposed that uses a support vector machine (SVM) with a customized kernel function. To build the kernel function, we applied a new sequence-matching algorithm that measures the similarities between passing and failing executions. We conducted some experiments to assess the proposed method. The results show that our method can effectively improve the performance of SBFL techniques.