ContribAlign improves open source contribution guidelines automatically
When CONTRIBUTING Files Fall Short: Revising OSS Contribution Guidelines with Repository Evidence
Software Engineering
Summary
Open source projects often have instructions for new contributors that can be out of date or missing important details. The authors created ContribAlign, a tool that checks these guidelines, finds missing or wrong information, and updates them using evidence from the project and artificial intelligence. Their tests show ContribAlign produces clearer, more helpful instructions that better support newcomers. This makes it easier for people to contribute to open source software.
What this means in practice
- •For open source project maintainers: Automatically update CONTRIBUTING files to improve clarity and completeness for new contributors.
- •For software development teams: Maintain accurate project contribution instructions by using evidence-based automated revisions to reduce onboarding overhead.
Authors
Kai Zheng, XinGuo Zhou, Xin Tan
Abstract
Contribution guidelines help newcomers participate in open-source software projects, yet often contain incomplete or outdated information. Existing studies characterize newcomers' information needs and documentation gaps, but revising these guidelines requires recovering and validating project-specific knowledge from scattered sources. We present ContribAlign, a framework for automatically revising CONTRIBUTING files through category-guided evidence integration. A qualitative analysis yields a taxonomy of 30 fine-grained guidance types across six contribution categories. ContribAlign uses a classifier to diagnose coverage of the six categories in the original guidance, then uses the taxonomy to collect and organize project-specific evidence. It filters detected conflicts and uses a large language model to generate category-level revisions. The classifier achieves a weighted F1 score of 0.764, improving on the existing classifier by 0.156, while the verifier detects factual conflicts with 97.3% precision and 91.5% estimated recall. Human evaluation shows that ContribAlign achieves the highest overall quality among the original guidelines and three baselines, with significant improvements in usefulness, beginner-friendliness, and completeness. These results suggest that ContribAlign can help maintain more accurate and useful contribution guidelines.