Improving Weighted-SBFL by Blocking Spectrum

Debugging is a costly process in software development, and computer-aided debugging is expected to reduce the cost. In debugging, fault localization is used to identify the location of potentially faulty code. Spectrum-based fault localization (SBFL) identifies program statements that contain faults based on program spectra collected during the execution of the test cases. Conventional SBFL treats all test cases as having equal importance. A weighting technique that assigns importance to test cases based on the similarity of program spectra (where higher similarity indicates higher importance) has been proposed. However, this technique does not significantly improve fault localization accuracy. We attribute this lack of improvement to the presence of sequential program statements, which negatively affect the weighting. In this study, we apply blocking and the weighting of spectra to improve accuracy. We conduct experiments to compare the proposed technique with conventional SBFL and a recent SBFL technique. We show that the proposed technique identifies faulty program statements with higher accuracy than previous SBFL techniques. Weighting based on the similarity of spectra after blocking is thus effective.