@inproceedings{pavel-etal-2025-swe,
    title = "{SWE}-{MERA}: A Dynamic Benchmark for Agenticly Evaluating Large Language Models on Software Engineering Tasks",
    author = "Pavel, Adamenko  and
      Mikhail, Ivanov  and
      Valeev, Aidar  and
      Levichev, Rodion  and
      Zadorozhny, Pavel  and
      Lopatin, Ivan  and
      Babaev, Dmitrii  and
      Fenogenova, Alena  and
      Malykh, Valentin",
    editor = {Habernal, Ivan  and
      Schulam, Peter  and
      Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.emnlp-demos.30/",
    doi = "10.18653/v1/2025.emnlp-demos.30",
    pages = "440--452",
    ISBN = "979-8-89176-334-0",
    abstract = "The rapid advancement of Large Language Models (LLMs) in software engineering has revealed critical limitations in existing benchmarks, particularly the widely used SWE-bench dataset. Recent studies have uncovered severe data contamination issues, e.g., SWE-bench reports 32.67{\%} of successful patches involve direct solution leakage and 31.08{\%} pass due to inadequate test cases. We introduce SWE-MERA, a dynamic, continuously updated benchmark designed to address these fundamental challenges through an automated collection of real-world GitHub issues and rigorous quality validation. Our approach implements a reliable pipeline that ensures quality while minimizing contamination risks, resulting in approximately 10,000 potential tasks with 728 samples currently available. Evaluation using the Aider coding agent demonstrates strong discriminative power in state-of-the-art models. We report performance across a dozen recent LLMs evaluated on tasks collected between September 2024 and June 2025."
}