Code2Doc: A Quality-First Curated Dataset for Code Documentation

TL;DR

This paper introduces Code2Doc, a high-quality, curated dataset for function-level code documentation across five programming languages, aiming to improve automatic documentation models by providing cleaner, more reliable training data.

Contribution

The paper presents a novel, multi-stage curation pipeline that filters and refines code-documentation pairs, resulting in a high-quality dataset for code documentation generation.

Findings

Fine-tuning on Code2Doc improves BLEU by 29.47%

Dataset contains 13,358 high-quality pairs

Only 2.9% of samples are AI-generated

Abstract

The performance of automatic code documentation generation models depends critically on the quality of the training data used for supervision. However, most existing code documentation datasets are constructed through large scale scraping of public repositories with limited quality control. As a result, they often contain noisy documentation, extensive duplication, and increasing contamination from AI generated content. These issues weaken the supervision signal available to learning-based models and complicate evaluation. We introduce Code2Doc, a quality-first curated dataset for function-level code documentation generation. Code2Doc consists of 13,358 high-quality function-documentation pairs extracted from widely used open-source repositories spanning five programming languages: Python, Java, TypeScript, JavaScript, and C++. The dataset is constructed using a four-stage curation pipeline that enforces documentation completeness and clarity, filters functions based on structural and complexity criteria, removes exact and near-duplicate code, and identifies documentation likely to be AI generated. Starting from 52,069 extracted candidates, only 25.6% satisfy all quality constraints. We provide a detailed analysis of the resulting dataset, which achieves a mean documentation quality score of 6.93 out of 10. Overall, 86.9% of samples contain explicit type annotations, and only 2.9% are flagged as potentially AI generated. Baseline experiments show that fine-tuning a large language model on Code2Doc yields relative improvements of 29.47% in BLEU and 24.04% in ROUGE-L over zero shot performance, despite the modest dataset size. We release both the dataset and the full curation pipeline to support reproducible research on automatic code documentation generation.