Automated Generation of Issue-Reproducing Tests by Combining LLMs and Search-Based Testing

Issue-reproducing tests fail on buggy code and pass once a patch is applied, thus increasing developers' confidence that the issue has been resolved and will not be re-introduced. However, past research has shown that developers often commit patches without such tests, making the automated generation of issue-reproducing tests an area of interest. We propose BLAST, a tool for automatically generating issue-reproducing tests from issue-patch pairs by combining LLMs and search-based software testing (SBST). For the LLM part, we complement the issue description and the patch by extracting relevant context through git history analysis, static analysis, and SBST-generated tests. For the SBST part, we adapt SBST for generating issue-reproducing tests; the issue description and the patch are fed into the SBST optimization through an intermediate LLM-generated seed, which we deserialize into SBST-compatible form. BLAST successfully generates issue-reproducing tests for 151/426 (35.4%) of the issues from a curated Python benchmark, outperforming the state-of-the-art (23.5%). Additionally, to measure the real-world impact of BLAST, we built a GitHub bot that runs BLAST whenever a new pull request (PR) linked to an issue is opened, and if BLAST generates an issue-reproducing test, the bot proposes it as a comment in the PR. We deployed the bot in three open-source repositories for three months, gathering data from 32 PRs-issue pairs. BLAST generated an issue-reproducing test in 11 of these cases, which we proposed to the developers. By analyzing the developers' feedback, we discuss challenges and opportunities for researchers and tool builders. Data and material: https://doi.org/10.5281/zenodo.16949042