FixCheck: A Tool for Improving Patch Correctness Analysis

Patch correctness assessment aims at effectively detecting overfitted patches, i.e., patches that causes all tests to pass but do not actually fix the bug. Although several automated techniques for assessing patch correctness have been proposed, these techniques typically yield a binary result (correct/incorrect) without providing any additional information explaining the rationale behind the decision of classifying a patch as correct or incorrect. This tool demo paper presents FixCheck, a tool based on static analysis, random testing and Large Language Models (LLMs), that seeks to improve the patch correctness analysis process by providing fault-revealing tests for potentially incorrect patches. To this end, FixCheck first employs static analysis and random testing to generate a comprehensive set of test cases that are similar to the original failing test case. Then, FixCheck relies on LLMs to derive meaningful assertions for each new test case. Finally, FixCheck executes the generated tests, and those that fail are selected and prioritized based on their likelihood of revealing a defect in the patch.