CodeRepoQA: A Large-scale Benchmark for Software Engineering Question Answering

TL;DR

CodeRepoQA is a comprehensive, large-scale benchmark dataset for evaluating software engineering question-answering capabilities of language models across multiple programming languages and scenarios.

Contribution

Introduces a novel, large-scale, multi-language benchmark dataset for repository-level software engineering question answering, with extensive data collection and analysis.

Findings

LLMs have limitations in software engineering QA tasks.

Medium-length contexts improve LLM performance.

Benchmark is publicly available for research use.

Abstract

In this work, we introduce CodeRepoQA, a large-scale benchmark specifically designed for evaluating repository-level question-answering capabilities in the field of software engineering. CodeRepoQA encompasses five programming languages and covers a wide range of scenarios, enabling comprehensive evaluation of language models. To construct this dataset, we crawl data from 30 well-known repositories in GitHub, the largest platform for hosting and collaborating on code, and carefully filter raw data. In total, CodeRepoQA is a multi-turn question-answering benchmark with 585,687 entries, covering a diverse array of software engineering scenarios, with an average of 6.62 dialogue turns per entry. We evaluate ten popular large language models on our dataset and provide in-depth analysis. We find that LLMs still have limitations in question-answering capabilities in the field of software engineering, and medium-length contexts are more conducive to LLMs' performance. The entire benchmark is publicly available at https://github.com/kinesiatricssxilm14/CodeRepoQA.