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 and dynamic information about code elements. This is achieved by giving more importance to code elements that include mathematical operators compared to other types of elements (e.g., declaration, selection, iteration, or function call) and appear in failed tests. The intuition is that these elements are more likely to have bugs than others. The proposed approach is applicable to any SBFL formula without requiring any modifications to their structures because the weighting is done on the ranking list and not on the formulas. The experimental results of a preliminary study show that our approach achieved a much better performance in terms of average ranking compared to the underlying SBFL formulas. It also improved the Top-N categories; it doubled the number of cases in which the faulty method became the top-ranked element, and in all cases the fault became part of Top-5 of the ranking list.