Enhancing Spectrum Based Fault localization Via Emphasizing Its Formulas With Importance Weight

Abstract

Spectrum-Based Fault Localization (SBFL) computes suspicion scores, using risk evaluation formulas, for program elements (e.g., state-ments, methods, or classes) by counting how often each element is executed or not executed by passing versus failing test cases. The elements are then ranked from most suspicious to least suspicious based on their scores. The elements with the highest scores are thought to be the most faulty. The final ranking list of program elements helps testers during the debugging process when attempting to locate the source of a bug in the program under test. In this paper, we present an approach that gives more importance to pro-gram elements that are executed by more failed test cases compared to other elements. In essence, we are emphasizing the failing test cases factor because there are comparably much less failing tests than passing ones. We multiply each element's suspicion score ob-tained by an SBFL formula by this importance weight, which is the ratio of covering failing tests over all failing tests. The proposed approach can be applied to SBFL formulas without modifying their structures. The experimental results of our study show that our approach achieved a better performance in terms of average ranking compared to the underlying SBFL formulas. It also improved the Top- N categories and increased the number of cases in which the faulty method became the top-ranked element.