Toward Linking Declined Proposals and Source Code: An Exploratory Study on the Go Repository

2026-02-10

AI Summary

This study addresses a critical gap in software traceability research by systematically investigating the linkage between rejected proposals and their associated source code—a dimension largely overlooked in prior work that predominantly focuses on accepted contributions. To bridge this gap, the authors propose an automated traceability link generation pipeline leveraging large language models (LLMs), grounded in empirical analysis of official Go language proposal discussions and case studies of failed proposals. Evaluated on a Go proposal dataset, the approach achieves a linking granularity selection accuracy of 0.836 and an average precision of 0.643 for generated links. The analysis further uncovers key challenges such as discussion redundancy and ambiguous information, highlighting the untapped value of rejected proposals in understanding software evolution and design rationale.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Traceability links are key information sources for software developers, connecting software artifacts (e.g., linking requirements to the corresponding source code). In open-source software (OSS) projects, such links play an important role, particularly between the contributions (e.g., GitHub issues) and the corresponding source code. Through these links, developers can trace the discussions in contributions and uncover design rationales, constraints, and security concerns. Previous studies have mainly examined accepted contributions, while those declined after discussion have been overlooked. The discussions behind declined contributions contain valuable design rationales and implicit knowledge about software decision-making, as the reasons behind the decline often reveal the criteria used to judge what should or should not be implemented. In this study, we present the first attempt to establish traceability links between declined contributions and related source code. We propose an initial linking approach and conduct an empirical analysis of the generated links to discuss factors affecting link generation. As our dataset, we use proposals from the official Go repository, which are GitHub issues used to propose new features or language changes. To link declined proposals to source code, we designed an LLM-driven pipeline. Our results showed that the pipeline selected the correct granularity for each declined proposal with an accuracy of 0.836, and generated correct links at that granularity with a mean precision of 0.643. To clarify the challenges of linking declined proposals, we performed a failure analysis. In the declined proposals where the pipeline failed to generate links, the discussions were often redundant and lacked concrete information (e.g., how the feature should be implemented).

Problem

• traceability
• declined proposals
• source code
• open-source software
• software artifacts

Innovation

• traceability links
• declined proposals
• LLM-driven pipeline
• open-source software
• empirical analysis