  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.
