RepoQA
RepoQA is a benchmark for evaluating long-context code understanding capabilities of Large Language Models through the Searching Needle Function (SNF) task, where LLMs must locate specific functions in code repositories using natural language descriptions. The benchmark contains 500 code search tasks spanning 50 repositories across 5 modern programming languages (Python, Java, TypeScript, C++, and Rust), tested on 26 general and code-specific LLMs to assess their ability to comprehend and navigate code repositories.
Phi-3.5-MoE-instruct from Microsoft currently leads the RepoQA leaderboard with a score of 0.850 across 2 evaluated AI models.
What RepoQA measures
RepoQA is a text benchmark that evaluates large language models on long context, reasoning, and code tasks. LLM Stats tracks 2 models on this benchmark, with a maximum possible score of 1. Current average across reported models is 0.8, with the leader reaching 0.8.
Compare leaders on the best AI for long context, best AI for reasoning and best AI for code leaderboards.
Publication
- Paper
- RepoQA: Evaluating Long Context Code Understanding
- Authors
- Jiawei Liu, Jia Le Tian, Vijay Daita, Yuxiang Wei, and 4 others
- Published
- arXiv
- 2406.06025
Abstract
Recent advances have been improving the context windows of Large Language Models (LLMs). To quantify the real long-context capabilities of LLMs, evaluators such as the popular Needle in a Haystack have been developed to test LLMs over a large chunk of raw texts. While effective, current evaluations overlook the insight of how LLMs work with long-context code, i.e., repositories. To this end, we initiate the RepoQA benchmark to evaluate LLMs on long-context code understanding. Traditional needle testers ask LLMs to directly retrieve the answer from the context without necessary deep understanding. In RepoQA, we built our initial task, namely Searching Needle Function (SNF), which exercises LLMs to search functions given their natural-language description, i.e., LLMs cannot find the desired function if they cannot understand the description and code. RepoQA is multilingual and comprehensive: it includes 500 code search tasks gathered from 50 popular repositories across 5 modern programming languages. By evaluating 26 general and code-specific LLMs on RepoQA, we show (i) there is still a small gap between the best open and proprietary models; (ii) different models are good at different languages; and (iii) models may understand code better without comments.
Phi-3.5-MoE-instruct leads with 85.0%, followed by Phi-3.5-mini-instruct at 77.0%.
Progress Over Time
Interactive timeline showing model performance evolution on RepoQA
RepoQA Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 | Microsoft | 60B | — | — | ||
| 2 | Microsoft | 4B | — | — |
FAQ
Common questions about RepoQA.
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