{"ID":2837341,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.18782","arxiv_id":"2511.18782","title":"Summary-Mediated Repair: Can LLMs use code summarisation as a tool for program repair?","abstract":"Large Language Models (LLMs) often produce code with subtle implementation-level bugs despite strong benchmark performance. These errors are hard for LLMs to spot and can have large behavioural effects; yet when asked to summarise code, LLMs can frequently surface high-level intent and sometimes overlook this low-level noise. Motivated by this, we propose summary-mediated repair, a prompt-only pipeline for program repair that leverages natural-language code summarisation as an explicit intermediate step, extending previous work that has already shown code summarisation to be a useful intermediary for downstream tasks. We evaluate our method across eight production-grade LLMs on two function level benchmarks (HumanEvalPack and MBPP), comparing several summary styles against a direct repair baseline. Error-aware diagnostic summaries consistently yield the largest gains - repairing up to 65% of unseen errors, on average of 5% more than the baseline - though overall improvements are modest and LLM-dependent. Our results position summaries as a cheap, human-interpretable diagnostic artefact that can be integrated into program-repair pipelines rather than a stand-alone fix-all.","short_abstract":"Large Language Models (LLMs) often produce code with subtle implementation-level bugs despite strong benchmark performance. These errors are hard for LLMs to spot and can have large behavioural effects; yet when asked to summarise code, LLMs can frequently surface high-level intent and sometimes overlook this low-level...","url_abs":"https://arxiv.org/abs/2511.18782","url_pdf":"https://arxiv.org/pdf/2511.18782v1","authors":"[\"Lukas Twist\"]","published":"2025-11-24T05:33:38Z","proceeding":"cs.SE","tasks":"[\"cs.SE\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}