标题： 揭开功能请求的神秘面纱：利用大语言模型在开源软件中优化功能请求 显示英文标题

标题： Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software

摘要： 软件应用（apps）在各个领域的日益普及和广泛应用推动了行业的快速增长。 随着这种增长，快速变化的市场导致软件需求不断演变。 这些需求通常基于用户提供的功能请求和改进建议，这些请求通常以自然语言（NL）形式提供。 然而，这些请求常常存在歧义和不完整等缺陷，使得它们难以解释。 传统的验证方法（例如访谈和研讨会）有助于澄清这些缺陷，但在像开源软件（OSS）这样的去中心化环境中并不实际，因为变更请求来自GitHub等平台上的不同用户。 本文提出了一种新颖的方法，利用大型语言模型（LLMs）来检测和精炼功能请求中的自然语言缺陷。 我们的方法自动化地识别模糊和不完整的请求，并生成澄清问题（CQs）以提高它们对开发人员的有用性。 为了评估其有效性，我们将该方法应用于现实世界的OSS功能请求，并将其性能与人工标注进行比较。 此外，我们还对GitHub开发人员进行了访谈，以深入了解他们对自然语言缺陷的看法、他们用来解决这些缺陷的策略以及缺陷对下游软件工程（SE）任务的影响。 显示英文摘要

摘要： The growing popularity and widespread use of software applications (apps) across various domains have driven rapid industry growth. Along with this growth, fast-paced market changes have led to constantly evolving software requirements. Such requirements are often grounded in feature requests and enhancement suggestions, typically provided by users in natural language (NL). However, these requests often suffer from defects such as ambiguity and incompleteness, making them challenging to interpret. Traditional validation methods (e.g., interviews and workshops) help clarify such defects but are impractical in decentralized environments like open-source software (OSS), where change requests originate from diverse users on platforms like GitHub. This paper proposes a novel approach leveraging Large Language Models (LLMs) to detect and refine NL defects in feature requests. Our approach automates the identification of ambiguous and incomplete requests and generates clarification questions (CQs) to enhance their usefulness for developers. To evaluate its effectiveness, we apply our method to real-world OSS feature requests and compare its performance against human annotations. In addition, we conduct interviews with GitHub developers to gain deeper insights into their perceptions of NL defects, the strategies they use to address these defects, and the impact of defects on downstream software engineering (SE) tasks.