Poster: Improving Spectrum Based Fault Localization For Python Programs Using Weighted Code Elements

In this paper, we present an approach for improving Spectrum-based fault localization (SBFL) by integrating static information about code elements and dynamic/execution information of code elements. This is achieved by giving more importance to code elements that have mathematical operators compared to other types of elements (e.g., declaration, selection, iteration, or function call) and appear in failed tests because these elements are more likely to have bugs than others. The proposed approach is applicable to SBFL formulas without requiring any modifications to 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.

Keywords:     Debugging, fault localization, spectrum-based fault localization, importance weight, suspiciousness score.