Evaluating LLM-Based Regression Test Generation

Large Language Models (LLMs) have shown tremendous promise in automated software engineering. In this paper, we investigate the opportunities of LLMs for just-in-time regression test generation for programs, like parsers, interpreters, or compilers, that take highly structured, human-readable inputs. When a new bug fix or code change is committed, the repository (as part of the CI/CD workflow) runs an LLM for a few minutes to generate regression test cases for that commit that exercise the changed code and potentially trigger any bugs. Specifically, we investigate LLM-based regression test generation as a machine translation task that takes the developer-provided commit message, the code change, and the name of the input format (e.g., XML) and produces regression test cases for the described change in the given input format. In our experiments testing 72 commits to Mujs, Libxml2, Poppler, JerryScript, Z3, PHP, JQ, and MicroPython, our feedback-directed, zero-shot LLM-based prototype Cleverest performed well, even if we did not provide the code change. In under 2 minutes, on average, Cleverest found as many bugs as the state-of-the-art directed greybox fuzzer WAFLGo in 24 hours, even though WAFLGo started with a commit-reaching seed corpus in the majority of cases. If we amplify the Cleverest-generated test cases using those as a seed corpus in coverage-guided greybox fuzzing, the number of bugs found doubles. We call the integration with fuzzing as ClevFuzz. In addition, we found that some commit messages are more expressive than others, thus we wonder how this impacts the effectiveness of Cleverest. Our results above demonstrate that Cleverest picks up on the change intention. For instance, if the commit message describes that this patch changes how floating point variables are treated in the Mujs JavaScript interpreter, then Cleverest generates JavaScript programs that contain floating point variables. To study the impact of expressiveness, we change the commit messages minimally to reduce and increase the information in the commit message, respectively, and find a substantial impact on effectiveness. For instance, adding 17 words on average (max. 43) to make ineffective commit messages more expressive significantly increased the number of bugs found.