OpenClassGen: A Large-Scale Corpus of Real-World Python Classes for LLM Research

[2504.15564] OpenClassGen: A Large-Scale Corpus of Real-World Python Classes for LLM Research Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Software Engineering arXiv:2504.15564 (cs) [Submitted on 22 Apr 2025 ( v1 ), last revised 30 Apr 2026 (this version, v3)] Title: OpenClassGen: A Large-Scale Corpus of Real-World Python Classes for LLM Research Authors: Musfiqur Rahman , SayedHassan Khatoonabadi , Emad Shihab View a PDF of the paper titled OpenClassGen: A Large-Scale Corpus of Real-World Python Classes for LLM Research, by Musfiqur Rahman and 2 other authors View PDF HTML (experimental) Abstract: Existing class-level code generation datasets are either synthetic (ClassEval: 100 classes) or insufficient in scale for modern training needs (RealClassEval: 400 classes), hindering robust evaluation and empirical analysis. We present OpenClassGen, a large-scale corpus of 324,843 Python classes extracted from 2,970 engineered open-source projects. Each entry pairs a human-written class with its corresponding skeleton, which comprises class and method signatures with associated docstrings, and is enriched with 27 static code metrics covering complexity, coupling, cohesion, and inheritance properties. Unlike prior benchmarks that require repository-level context resolution, OpenClassGen provides self-contained class skeletons that serve as complete generation specifications. We demonstrate the corpus's utility by evaluating three LLMs (GPT-o4-mini, Claude-4-Sonnet, Qwen-3-Coder) on a curated, executable subset of 300 classes, enriched with test suites achieving 58% branch coverage. Results show strong semantic similarity (CodeBERTScore-F3: 0.89) but moderate functional correctness (pass rate: 0.33), with substantial variance across models. This variance, along with diverse class characteristics, confirms that OpenClassGen enables meaningful differentiation of LLM capabilities. The dataset supports diverse use cases, including fine-tuning, retrieval-augmented generation, difficulty modelling, and failure mode analysis. The complete dataset and curation scripts are publicly available at this https URL . Comments: This paper has been accepted for publication at the 30th International Conference on Evaluation and Assessment in Software Engineering (EASE 2026) AI models/data track Subjects: Software Engineering (cs.SE) ; Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2504.15564 [cs.SE] (or arXiv:2504.15564v3 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2504.15564 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Musfiqur Rahman [ view email ] [v1] Tue, 22 Apr 2025 03:33:57 UTC (67 KB) [v2] Thu, 9 Apr 2026 04:00:27 UTC (72 KB) [v3] Thu, 30 Apr 2026 17:27:03 UTC (72 KB) Full-text links: Access Paper: View a PDF of the paper titled OpenClassGen: A Large-Scale Corpus of Real-World Python Classes for LLM Research, by Musfiqur Rahman and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.SE < prev | next > new | recent | 2025-04 Change to browse by: cs cs.AI cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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