Impact of Combining Syntactic and Semantic Similarities on Patch Prioritization while using the Insertion Mutation Operators

TL;DR

This study evaluates how combining syntactic and semantic similarities using various measures impacts patch prioritization, successfully fixing more bugs earlier than previous methods with high precision.

Contribution

It introduces a novel approach that combines syntactic and semantic features for patch prioritization, significantly improving bug fixing efficiency using Insertion mutation operators.

Findings

Successfully fixed 6 bugs before incorrect plausible patches.

Solved 25 bugs in total, the highest compared to prior approaches.

Achieved 100% precision in generated fixes.

Abstract

Patch prioritization ranks candidate patches based on their likelihood of being correct. The fixing ingredients that are more likely to be the fix for a bug, share a high contextual similarity. A recent study shows that combining both syntactic and semantic similarity for capturing the contextual similarity, can do better in prioritizing patches. In this study, we evaluate the impact of combining the syntactic and semantic features on patch prioritization using the Insertion mutation operators. This study inspects the result of different combinations of syntactic and semantic features on patch prioritization. As a pilot study, the approach uses genealogical similarity to measure the semantic similarity and normalized longest common subsequence, normalized edit distance, cosine similarity, and Jaccard similarity index to capture the syntactic similarity. It also considers Anti-Pattern to filter out the incorrect plausible patches. The combination of both syntactic and semantic similarity can reduce the search space to a great extent. Also, the approach generates fixes for the bugs before the incorrect plausible one. We evaluate the techniques on the IntroClassJava benchmark using Insertion mutation operators and successfully generate fixes for 6 bugs before the incorrect plausible one. So, considering the previous study, the approach of combining syntactic and semantic similarity can able to solve a total number of 25 bugs from the benchmark, and to the best of our knowledge, it is the highest number of bugs solved than any other approach. The correctness of the generated fixes are further checked using the publicly available results of CapGen and thus for the generated fixes, the approach achieves a precision of 100%