Isolating Suspiciousness from Spectrum-Based Fault Localization Techniques

Spectrum-based fault localization (SBFL) is one of the most promising fault localization approaches, which normally uses the failed and passed program spectrum to evaluate the risks for all program entities. However, it does not explicitly distinguish the different degree in definiteness between the information associated with the failed spectrum and the passed spectrum, which may result in an unreliable location of faults. Thus in this paper, we propose a refinement method to improve the accuracy of the predication by SBFL, through eliminating the indefinite information. Our method categorizes all statements into two groups according to their different suspiciousness, and then uses different evaluation schemes for these two groups. In this way, we can reduce the use of the unreliable information in the ranking list, and finally provide a more precise result. Experimental study shows that for some SBFL techniques, our method can significantly improve their performance in some situations, and in other cases, it can still remain the techniques' original performance.