More Reliable Test Suites for Dynamic APR by using Counterexamples

Abstract

Dynamic automated program repair (APR) techniques, which use test suites for bug localization and evaluating candidate patches, have promising results. However, many studies show that machine-generated patches with dynamic APR tools are not always reliable. Recent studies show that enhancing test suites by adding tests will help dynamic APR tools generate more reliable patches. We evaluate the effectiveness of minimally enhancing test suites by adding counterexamples for repaired programs that suffer from test overfitting. We use formal methods as an independent standard for evaluating patches' correctness and for generating counterexamples. Techniques for evaluating patch correctness (both with human reviewers and formal methods) can create false negatives, meaning that the repaired program is correct but is deemed incorrect. A counterexample is a good way to check on reviewer decisions about correctness. Our study evaluated 256 repaired but not verified programs (from the buggy Java+JML dataset); the repairs were generated by seven state-of-the-art dynamic APR tools. Our results show that the counterexample generated by the OpenJML tool could correctly classify all these programs into the categories of “test overfitting” and “false negatives.” After adding tests based on the counterexamples to the test suites, we ran the APR tools on the original buggy programs again and found that: (1) the APR tools were able to generate about 27.3% more correct patches with the enhanced test suite, and (2) the enhanced test suite resulted in the APR tools generating about 83.6% fewer overfitted patches.