Towards Better Requirements from the Crowd: Developer Engagement with Feature Requests in Open Source Software

2025-07-17

AI Summary

Functional requests in open-source projects often suffer from ambiguity and information gaps in natural language, leading to developer misinterpretation and implementation deviations. This paper presents the first systematic empirical study of clarification dialogues surrounding feature requests on platforms such as GitHub. Leveraging qualitative coding, conversation analysis, and NLP techniques, we analyze thousands of issue reports and their associated discussions. Our findings reveal: (1) over 70% of requests exhibit linguistic or informational deficiencies, yet explicit clarification interactions remain rare; (2) developers prioritize verifying user intent and project alignment over eliciting technical specifics—establishing a “goal alignment over detail clarification” practice paradigm; and (3) we propose a collaboration optimization framework centered on intent understanding and contextual co-construction between users and developers. This work provides both theoretical foundations and actionable guidelines for improving requirements communication efficiency and software quality.

Technology Category

• Natural Language Processing: Discourse, Pragmatics & Argument Mining
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Knowledge Representation and Reasoning: Preferences

Application Category

• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

As user demands evolve, effectively incorporating feature requests is crucial for maintaining software relevance and user satisfaction. Feature requests, typically expressed in natural language, often suffer from ambiguity or incomplete information due to communication gaps or the requester's limited technical expertise. These issues can lead to misinterpretation, faulty implementation, and reduced software quality. While seeking clarification from requesters is a common strategy to mitigate these risks, little is known about how developers engage in this clarification process in practice-how they formulate clarifying questions, seek technical or contextual details, align on goals and use cases, or decide to close requests without attempting clarification. This study investigates how feature requests are prone to NL defects (i.e. ambiguous or incomplete) and the conversational dynamics of clarification in open-source software (OSS) development, aiming to understand how developers handle ambiguous or incomplete feature requests. Our findings suggest that feature requests published on the OSS platforms do possess ambiguity and incompleteness, and in some cases, both. We also find that explicit clarification for the resolution of these defects is uncommon; developers usually focus on aligning with project goals rather than resolving unclear text. When clarification occurs, it emphasizes understanding user intent/goal and feasibility, rather than technical details. By characterizing the dynamics of clarification in open-source issue trackers, this work identifies patterns that can improve user-developer collaboration and inform best practices for handling feature requests effectively.

Problem

• Address ambiguity and incompleteness in feature requests
• Study developer engagement in clarifying feature requests
• Improve user-developer collaboration in open-source software

Innovation

• Analyzes NL defects in feature requests
• Studies developer clarification dynamics
• Focuses on user intent and feasibility