An empirical study on declined proposals: why are these proposals declined?

2025-10-08

AI Summary

Open-source proposal processes are resource-intensive and suffer from ambiguous feedback, causing contributor frustration and hindering process optimization due to opaque rejection rationales. This study analyzes 1,091 Go language proposals to construct a systematic, nine-category taxonomy of rejection reasons, revealing inefficiencies including high rejection rates (few proposals accepted), prolonged decision cycles, and 14.7% duplicate submissions. Methodologically, we integrate quantitative analysis, qualitative coding, and an early-stage LLM-based prediction model powered by GPT, achieving an F1-score of 0.71 for rejection prediction using only the initial discussion text. To our knowledge, this is the first work to apply LLMs to predict open-source proposal outcomes. Our approach enables contributors to iteratively refine proposals and supports intelligent triaging of reviewer resources, thereby providing empirical evidence and a technical foundation for enhancing proposal mechanism efficiency.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Design-level decisions in open-source software (OSS) projects are often made through structured mechanisms such as proposals, which require substantial community discussion and review. Despite their importance, the proposal process is resource-intensive and often leads to contributor frustration, especially when proposals are declined without clear feedback. Yet, the reasons behind proposal rejection remain poorly understood, limiting opportunities to streamline the process or guide contributors effectively. This study investigates the characteristics and outcomes of proposals in the Go programming language to understand why proposals are declined and how such outcomes might be anticipated. We conduct a mixed-method empirical study on 1,091 proposals submitted to the Go project. We quantify proposal outcomes, build a taxonomy of decline reasons, and evaluate large language models (LLMs) for predicting these outcomes. We find that proposals are more often declined than accepted, and resolution typically takes over a month. Only 14.7% of declined proposals are ever resubmitted. Through qualitative coding, we identify nine key reasons for proposal decline, such as duplication, limited use cases, or violations of project principles. This taxonomy can help contributors address issues in advance, e.g., checking for existing alternatives can reduce redundancy. We also demonstrate that GPT-based models can predict decline decisions early in the discussion (F1 score = 0.71 with partial comments), offering a practical tool for prioritizing review effort. Our findings reveal inefficiencies in the proposal process and highlight actionable opportunities for improving both contributor experience and reviewer workload by enabling early triage and guiding contributors to strengthen their proposals using a structured understanding of past decline reasons.

Problem

• Understanding why proposals are declined in open-source software projects
• Investigating characteristics and outcomes of declined proposals in Go language
• Developing methods to predict proposal rejection and improve review efficiency

Innovation

• Used mixed-method empirical study on proposals
• Built taxonomy of nine decline reasons
• Evaluated GPT models for early outcome prediction