  The instruction-following ability of Large Language Models (LLMs) has
cultivated a class of LLM-based systems capable of approaching complex tasks
such as making edits to large code repositories. Due to the high sensitivity
and unpredictability of LLM behavior in response to changes in prompting,
robust evaluation tools are needed to drive future iteration of these systems.
We propose RES-Q, a natural language instruction-based benchmark for evaluating
$\textbf{R}$epository $\textbf{E}$diting $\textbf{S}$ystems, 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 the instruction, navigate the repository to
gather relevant information, and construct an appropriate edit that satisfies
the specified criteria. We argue that evaluating LLMs in this way addresses
issues with traditional benchmarks and provides a more holistic assessment of a
model's abilities. We evaluate various state-of-the-art LLMs as language agents
in a repository-editing system built on Qurrent OS, our language agent
development software. Despite their 1% pass@1 performance difference on
HumanEval, we find Claude Sonnet 3.5 outperforms GPT-4o by 12% pass@1 on RES-Q,
indicating RES-Q's capacity to differentiate model capability as traditional
benchmarks approach saturation. We further investigate token efficiency,
performance relationships with existing benchmarks, and interesting disparities
between closed and open-source LLMs. Code and dataset are available at
https://github.com/Qurrent-AI/RES-Q.
