@inproceedings{a4027f68830341e88e5cdd0473920c3a,
title = "NERO: A Text-based Tool for Content Annotation and Detection of Smells in Feature Requests",
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.",
keywords = "Feature Request, Natural Language Process, Smell Detection",
author = "Fangwen Mu and Lin Shi and Wei Zhou and Yuanzhong Zhang and Huixia Zhao",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 28th IEEE International Requirements Engineering Conference, RE 2020 ; Conference date: 31-08-2020 Through 04-09-2020",
year = "2020",
month = aug,
doi = "10.1109/RE48521.2020.00056",
language = "英语",
series = "Proceedings of the IEEE International Conference on Requirements Engineering",
publisher = "IEEE Computer Society",
pages = "400--403",
editor = "Travis Breaux and Andrea Zisman and Samuel Fricker and Martin Glinz",
booktitle = "Proceedings - 28th IEEE International Requirements Engineering Conference, RE 2020",
address = "美国",
}