An empirical study on declined proposals: why are these proposals declined?
Masanari Kondo, Mahmoud Alfadel, Shane McIntosh, Yasutaka Kamei, Naoyasu Ubayashi
TL;DR
This study empirically characterizes design-level proposals in the Go language, revealing that declines are common (≈40%) and decisions take over a month on average. It builds a nine-item taxonomy of decline reasons and demonstrates that GPT-based models can predict declines early in discussions, with a practical recommendation to use only the first 25% of comments for cost-efficient triage. The work provides actionable guidance for maintainers and contributors to streamline review, reduce wasted effort, and improve proposal quality, while offering a replication package for future research. Overall, the findings highlight substantial inefficiencies in the proposal process and point to data-driven strategies for early screening and targeted guidance.
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.