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arXiv Machine Learning By Amir M. Ebrahimi, Mohammed Mehedi Hasan, Seyed Shayan Daneshvar, Jiahuei Lin, Gopi Krishnan Rajbahadur, Ahmed E. Hassan

Software Engineering LLMs: Why We Need Them and How Software Engineering Helps Build Them

Read the original on arXiv Machine Learning →

The paper discusses how software engineering (SE) expertise can guide the training of large language models (LLMs) for coding tasks. Using a case study of a 92‑billion‑parameter LLM, the authors created RefactorForge, a pipeline that leverages SE knowledge and tools to generate verified training data from 306 Python repositories. Fine‑tuning the model on this data improved performance on several benchmarks, including RefactorBench and SWE‑Bench ProMax, and the gains extended to other coding‑agent harnesses.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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