Abstract
Utilizing massive user feedback, e.g. feature requests from Bugzilla, JIRA, or GitHub, to motivate software evolution has become a new trend in RE community. However, manually understanding and analyzing feature requests from issue tracking systems is a time-consuming and labor-intensive task. In this paper, we present NERO (coNtent annotation and smElly Feature Requests detection), an automated tool to support analysts to understand the semantic meaning of feature requests and detect the smells in feature requests. It can also provide an overall score based on the smell detection results to help analysts quickly judge the quality of feature requests.
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