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RES-Q (RES-Q: Evaluating Code-Editing Large Language Model Systems at the Repository Scale)

Introduced by Beck Labash et al. in RES-Q: Evaluating Code-Editing Large Language Model Systems at the Repository Scale24 Jun 2024 archive 2025-07-28

RES-Q is a natural language instruction-based benchmark for evaluating Repository Editing Systems, which consists of 100 handcrafted repository editing tasks derived from real GitHub commits. Given an edit instruction and a code repository, RES-Q evaluates an LLM system’s ability to interpret edit instructions, gather information, and construct appropriate edits to the repository.

Benchmarks archive 2025-07-28

All 1 leaderboard whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.

First row (archive order)PaperCode
Code Generation RES-Q QurrentOS-coder + Claude 3.5 Sonnet pass@1 58.0 RES-Q: Evaluating Code-Editing Large Language Model... qurrent-ai/res-q 9 Compare

Papers archive 2025-07-28

1 shown of 1 paper with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 1. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.

DateSamples run Syntology
RES-Q: Evaluating Code-Editing Large Language Model Systems at the Repository Scale 1 9 24 Jun 2024 ran 6 of 6 samples (0 unverified)

Dataset loaders archive 2025-07-28

No loader listed in the archive.

Tasks archive 2025-07-28

License archive 2025-07-28

MIT

Modalities archive 2025-07-28

Languages archive 2025-07-28

Variants archive 2025-07-28

  • RES-Q

1 variant name, as the archive lists them.

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