Source-linked AI summary
When CONTRIBUTING Files Fall Short: Revising OSS Contribution Guidelines with Repository Evidence
Kai Zheng, XinGuo Zhou, Xin Tan
TL;DR
Contribution guidelines can be incomplete or outdated, and revising them requires project-specific evidence. ContribAlign diagnoses category coverage, gathers and verifies repository evidence, and generates revised guidelines; its revisions received higher human ratings than the original files and three baselines.
Problem
Contribution guidelines may be incomplete or outdated, while revision requires recovering and validating project-specific knowledge from scattered sources.
Source: §1 · p. 1Method
ContribAlign diagnoses six-category coverage, collects category-guided repository evidence, filters detected conflicts, and generates revisions.
Source: §1 · p. 2Results
33 of 40 comparisons selected ContribAlign revisions as preferred, and revised documents received higher human ratings than original files and three baselines.
Source: §1 · p. 2, §8 · p. 19Takeaways & Limitations
The findings highlight the potential of grounding document revision in category-specific repository evidence while filtering detected conflicts.
Source: §8 · p. 19Effectiveness may vary with project characteristics and programming ecosystems, and projects with less available evidence may require more maintainer input.
Source: §7.3 · p. 18
Abstract
from arXiv · showhide
Contribution guidelines help newcomers participate in open-source software projects, yet often contain incomplete or outdated information.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.
1 Introduction
CONTRIBUTING files can be incomplete or outdated, while maintaining them competes with other maintainer responsibilities. ContribAlign addresses the need for project-specific evidence to fill gaps and revise instructions.
1 Introduction
- Existing CONTRIBUTING files may omit guidance or retain outdated information, including gaps in task selection and community interaction.Prior research found that 52% of analyzed projects lacked guidance for at least three of six categories.Source: §1 · p. 1
- Identifying omissions or simplifying text does not provide the information needed to fill gaps or correct inaccurate instructions.Revisions require project-specific evidence from repository documentation, configurations, and development activities.Source: §1 · pp. 1–2
- Evidence collection must match varied guidance needs and repository sources, while conflicting or stale claims can require verification that cannot cover all guidance automatically.Relevant practices may appear in development activities as well as documentation.Source: §1 · p. 2
- ContribAlign diagnoses category coverage, gathers taxonomy-guided evidence from documents and repository activity, filters detected conflicts, and generates category-level revisions.The framework has four stages: document diagnosis, evidence collection, evidence verification, and category-wise generation and synthesis.Source: §1 · p. 2
- 0.764 weighted F1 was achieved for guidance coverage diagnosis; in human evaluation, ContribAlign revisions had the highest average ratings across four quality dimensions.The revisions were preferred in 33 of 40 comparisons.Source: §1 · p. 2
2 Background and Related Work
Contribution guidelines communicate project expectations and procedures as part of newcomer onboarding. Prior work documents gaps in documentation quality and automation, while ContribAlign targets the challenge of reliably revising contribution guidelines.
2.1 OSS Newcomer Onboarding and Contribution Guidance
- Contribution guidelines provide repository-level guidance on workflows and resources, addressing technical and social barriers newcomers encounter.Prior work identifies barriers such as finding tasks, setting up environments, and communicating with community members.Source: §2.1 · pp. 2–3
2.2 Quality and Maintenance of OSS Documentation
- Documentation problems include missing or incorrect information and poor organization, while initial CONTRIBUTING files can emphasize technical procedures over community development.As projects evolve, instructions may also need revision to remain applicable.Source: §2.2 · p. 3
2.3 Automated Documentation Analysis and Generation
- Prior automated approaches diagnose coverage gaps, detect outdated references, or improve expression and organization, but do not reliably revise complete, current, repository-consistent guidance.ContribAlign addresses this challenge by diagnosing missing guidance, collecting and verifying repository-specific evidence, and filtering detected conflicts before generation.Source: §2.3 · p. 3
3 Design Foundation
The design foundation extends six broad contribution categories with a fine-grained taxonomy to guide evidence collection and verification. The taxonomy was developed through qualitative analysis of existing contribution guidance.
3.1 Existing Classification Framework
- The six established categories describe broad contribution tasks but do not distinguish the specific information needed within each task.Fine-grained types can require different repository sources and verification procedures.Source: §3.1 · p. 4
3.2 Qualitative Analysis
- Open coding of 13,260 paragraph records from 500 repositories produced a codebook whose reliability assessment achieved Cohen’s κ of 0.74.Two authors independently coded 50 files for type discovery and another 50 for reliability assessment; disagreements were adjudicated by a third author.Source: §3.2 · p. 4
3.3 Fine-Grained Taxonomy
- The analysis yielded 30 fine-grained guidance types across six categories, specifying information to seek and guiding ContribAlign’s evidence collection.Contribution Flow types follow the sequence of contribution activities, while the other categories are primarily organized by guidance content.Source: §3.3 · p. 4
4 ContribAlign
ContribAlign revises contribution guidance through category diagnosis, taxonomy-guided evidence collection, conflict filtering, and category-wise generation and synthesis. Its evidence combines repository text with category-specific information from repository artifacts and activities.
4 ContribAlign
- ContribAlign classifies existing guidance into six categories, collects repository evidence, removes fragments with detected conflicts, and synthesizes category-specific drafts.The framework has four stages: diagnosis, evidence collection, verification, and generation and synthesis.Source: §4 · pp. 4–5
4.1 Category-Aware Guidance Diagnosis
- A pre-trained language model-based classifier assigns paragraphs to six categories or No Category Identified and builds a category coverage profile.Coverage means at least one paragraph is assigned to a category; it indicates presence, not adequacy or completeness.Source: §4.1 · pp. 5–6
4.2.1 Taxonomy-Guided RAG Retrieval.
- Taxonomy-guided retrieval uses dedicated queries for fine-grained guidance types, while a shared index covers readable files in the repository snapshot except the original CONTRIBUTING file.Retrieved blocks retain source paths and associations with their fine-grained types.Source: §4.2.1 · p. 6
4.2.2 Category-Specialized Direct Repository Evidence Processing.
- Category-specialized processing gathers direct evidence such as community resources, open issues, configurations, execution records, review expectations, and submission artifacts.Sources include the Code of Conduct, task labels, build and test records, pull request templates, and commit or licensing files.Source: §4.2.2 · p. 7
4.3 Evidence Verification
- The verifier splits guidance and retrieved text into fragments, checks automatically verifiable repository-specific claims against current repository evidence, and assigns support statuses.Checks include references to repository paths, issue labels, package scripts, and runtime versions; verification is limited to claims determinable from available evidence.Source: §4.3 · p. 8
- Only fragments with detected conflicts are removed; supported and unverified fragments are retained, so a supported status does not establish that every statement is verified.The verifier filters detected conflicts within its supported claim types rather than establishing the correctness of all retained content.Source: §4.3 · p. 8
4.4 Category-Wise Generation and Synthesis
- For each category, an LLM revises available original guidance or generates new guidance from evidence, and may omit a draft when evidence is insufficient.An additional LLM call screens retained uncategorized paragraphs for supplementary guidance; synthesis combines generated drafts without adding repository-specific information beyond them.Source: §4.4 · pp. 8–9
5.1 Research Questions
The study asks whether ContribAlign can classify existing contribution guidance and verify repository-specific claims accurately, and whether its revised guidelines compare favorably with existing and baseline-generated documents.
5.1 Research Questions
- RQ1 evaluates how accurately ContribAlign classifies existing contribution guidance into categories, comparing pretrained language models with Fronchetti et al.’s classifier and testing contextual inputs.The study examines whether adding headings and surrounding context affects classification.Source: §5.1 · p. 9
- RQ2 evaluates whether the rule-based verifier correctly labels claims in original guidance and retrieved evidence as supported, conflicting, or unverified.Its predictions are compared with manual annotations.Source: §5.1 · p. 9
- RQ3 compares revised guidelines from ContribAlign with existing files and three baselines on completeness, correctness, beginner-friendliness, and usefulness.The comparison uses human evaluation.Source: §5.1 · pp. 9–10
5.2 Experimental Setup
The experimental setup evaluates category classification, repository-evidence verification, and generated guideline quality using held-out data, repository snapshots, and human assessment.
5.2.1 RQ1: Contribution Guidance Classification.
- RQ1 trains and tests paragraph classifiers on 20,704 annotated instances from 500 repositories, split by repository into training, validation, and test partitions.The study compares five pretrained models with LinearSVC and tests paragraph-only, title-prefixed, and title-plus-context inputs; model selection uses validation weighted F1.Source: §5.2.1 · p. 10
5.2.2 RQ2: Evidence Verification.
- RQ2 evaluates the rule-based verifier on sampled fragments from original guidelines and retrieved evidence, using repository-contextualized manual labels as ground truth.The weighted analysis accounts for stratified sampling when estimating accuracy and per-class metrics.Source: §5.2.2 · pp. 10–11
5.2.3 RQ3: Generated Guideline Quality.
- For RQ3, the study updates evidence for 94 repositories and collects metadata, issues, recent pull requests and reviews, and available discussion channels.The target set comes from the RQ1 test repositories after excluding inaccessible, unavailable, and archived projects.Source: §5.2.3 · p. 11
- The three RQ3 generation baselines vary the repository context supplied to a shared revision prompt, while all methods use DeepSeek-V4.1-Flash.Direct LLM adds no retrieved repository evidence; Adapted LARCH selects code context, and Generic RAG retrieves the top 10 fragments with one category-list query.Source: §5.2.3 · p. 11
- The RQ3 evaluation compares guidelines on 20 repositories, sampled by programming language from 94 targets, using repository snapshots and 10 graduate-student evaluators unfamiliar with the projects.Two evaluators rated each repository’s original file and four generated candidates; documents were anonymized and randomized.Source: §5.2.3 · p. 12
- RQ3 ratings assess correctness, usefulness, beginner-friendliness, and completeness through five-point scales, with paired comparisons using Wilcoxon tests and Holm correction.Evaluators cross-check factual statements against repository files and assess whether instructions are actionable and sufficiently complete.Source: §5.2.3 · pp. 12–13
5.3 Results
The selected RoBERTa classifier outperformed LinearSVC on guidance classification, while the verifier detected conflicts conservatively. In human evaluation, ContribAlign rated highest overall, with stronger usefulness, beginner-friendliness, and completeness.
5.3.1 RQ1: Contribution Guidance Classification.
- 0.780 validation weighted F1 was reached by RoBERTa with paragraph, title, and surrounding context, making it the selected classifier.Adding paragraph titles improved performance over paragraph-only input for all five pretrained models; surrounding-context effects varied.Source: Table 3 · p. 13, §5.3.1 · p. 13
- Build Local Workspace F1 rose from 0.402 to 0.813; Choose a Task remained weakest, with recall rising from 0.163 to 0.407 as precision fell from 0.824 to 0.648.The recall increase for Choose a Task came with more false-positive predictions.Source: §5.3.1 · p. 13
5.3.2 RQ2: Evidence Verification.
- 97.3% precision and 91.5% estimated recall for conflicts; the verifier marks only definitive contradictions as conflicting and leaves uncertain cases unverified.The verifier’s conflicting-classification recall was 94.7% in the annotated sample and 91.5% after stratum weighting.Source: Table 6 · p. 14, §5.3.2 · pp. 14–15
5.3.1 RQ1: Contribution Guidance Classification.
- The selected RoBERTa classifier, using the paragraph, title, and surrounding context, achieved weighted F1 of 0.764, an absolute 0.156 improvement over LinearSVC on test-set classification.Source: §5.3.1 · p. 13, Table 4 · p. 14
5.3.3 RQ3: Generated Guideline Quality.
- Revised documents were longer on average, but Flesch Reading Ease scores were similar across all five document types.The measures were aggregated across 94 repositories and calculated on English prose after removing code and Markdown syntax.Source: Table 7 · p. 15, §5.3.3 · p. 15
- 33 of 40 evaluations selected ContribAlign as best; it had the highest mean scores on all four dimensions and significantly improved usefulness, beginner-friendliness, and completeness.Its correctness was comparable to the original guidelines and higher than the three generative baselines.Source: §5.3.3 · pp. 15, 17, Table 8 · p. 16
6 Discussion
The fine-grained taxonomy makes concrete information needs within broad contribution categories explicit and offers a reusable checklist for guidance. ContribAlign favors complete, evidence-supported revisions, though newcomers may prefer different levels of detail.
6.1 Understanding Contribution Guidance Beyond Broad Categories
- The taxonomy makes concrete information needs within broad contribution categories explicit, giving maintainers a checklist for writing or revising guidelines.It lets maintainers inspect fine-grained guidance types rather than only checking whether a broad category is present.Source: §6.1 · p. 17
- The taxonomy also structures evidence collection and category-wise generation in ContribAlign, while describing CONTRIBUTING content independently of any generation pipeline.Source: §6.1 · p. 17
6.2 Balancing Conciseness and Detail for Newcomers
- ContribAlign favors relatively complete, evidence-supported guidance, but a concise version may suit experienced contributors or projects with simple workflows.The discussion proposes future concise and detailed modes to reflect newcomers’ varied needs.Source: §6.2 · p. 17
7 Threats to Validity
The validity of the findings is bounded by evaluator judgments, component-level evaluation choices, and the repositories and model studied. These limits constrain interpretation of document quality and how broadly the results may generalize.
7.1 Construct Validity
- Document-quality scores reflect evaluator judgments, not newcomers’ direct use during contribution tasks.Computer science graduate students without prior contributions to the evaluated projects were recruited to provide a project-newcomer perspective.Source: §7.1 · p. 18
7.2 Internal Validity
- Classifier errors may affect category coverage judgments, while practices outside the verifier’s scope may remain in revised documents; maintainers should review revisions before adoption.Source: §7.2 · p. 18
- Without end-to-end component ablations, document-level evaluations cannot isolate how much each component contributes to document quality.The authors interpret document quality as an outcome of the complete pipeline.Source: §7.2 · p. 18
7.3 External Validity
- The generation evaluation uses 94 popular GitHub repositories after excluding inaccessible, archived, or unretrievable projects, so results mainly reflect relatively mature projects.Source: §7.3 · p. 18
- ContribAlign’s effectiveness may vary with project size, activity, hosting platform, programming language, and software ecosystem.Projects unlike those studied may offer less retrievable evidence, potentially requiring more direct maintainer input.Source: §7.3 · p. 18
- The human evaluation covers 20 of 94 target repositories, limiting the representativeness of its results despite stratification by primary language.Source: §7.3 · p. 18
- All generation methods use DeepSeek-V4.1-Flash, so conclusions are tied to that model and ContribAlign’s advantage could change with another model.Source: §7.3 · p. 18
8 Conclusion
ContribAlign revises CONTRIBUTING files by diagnosing guidance coverage, collecting repository evidence, verifying claims, and generating category-wise revisions. Its components show promising evaluation results, while revised documents receive higher human ratings than the original files and three baselines.
8 Conclusion
- ContribAlign revises CONTRIBUTING files through category-guided evidence integration, diagnosing coverage, collecting repository evidence, verifying claims, and generating category by category.Source: §8 · p. 19
- The classifier improves category diagnosis over prior work, and the verifier detects conflicting fragments with high precision.Source: §8 · p. 19
- Revised documents receive higher human ratings than the original files and three baselines.Source: §8 · p. 19