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

TL;DR

This paper examines how open-source developers handle ambiguous feature requests, revealing that clarification is infrequent and often focused on understanding goals rather than technical details, which impacts software development quality.

Contribution

It provides an empirical analysis of clarification practices in OSS, highlighting communication gaps and proposing patterns to improve feature request handling.

Findings

Feature requests often contain ambiguity and incompleteness.

Explicit clarification is infrequent among developers.

When clarification occurs, it focuses on user intent and feasibility.

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.