摘要: BACKGROUND: In an online issue tracking system, e.g., GitHub Issue Tracker, feature requests and the associated comment stream provide valuable crowd-generated knowledge for requirements elicitation. To decide whether a feature re... 展开 BACKGROUND: In an online issue tracking system, e.g., GitHub Issue Tracker, feature requests and the associated comment stream provide valuable crowd-generated knowledge for requirements elicitation. To decide whether a feature request should be accepted or not, stakeholders need to identify the comments for/against the feature and understand the two-sided opinions, which is time- and effort-consuming considering the abundant information embedded in lengthy comment stream per feature request. AIMS: This paper proposes VoteBot for automatically detecting stance (for/against) and summarizing the related opinions on a feature request, which can facilitate the decision making (i.e., voting) of feature requests. To our best knowledge, such an approach is previously unexplored for crowd-based requirements elicitation. METHOD: VoteBot is a relation-aware approach, which incorporates three types of relations among the comments or among the comment sentences to better understand the discussions about feature requests. Specifically, it extracts the reply-to relation among the comments, and incorporates it into a BERT-based classifier for stance detection. It also designs a graph-based ranking algorithm, and incorporates semantic relevance and argumentative relations for stance summarization. RESULTS: The automatic evaluation on 250 feature requests with 6,598 comments from five GitHub projects, and the evaluation with practitioners on five new projects, show the promising results. CONCLUSIONS: VoteBot is effective in stance detection and stance summarization, and potentially useful for understanding feature requests and associated discussions in real-world practice. 收起