ASE 2017

2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE 2017), October 30 – November 3, 2017, Urbana-Champaign, IL, USA

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Program Comprehension
Technical Research
Understanding Feature Requests by Leveraging Fuzzy Method and Linguistic Analysis
Lin Shi, Celia Chen, Qing Wang, Shoubin Li, and Barry Boehm
(Institute of Software at Chinese Academy of Sciences, China; University at Chinese Academy of Sciences, China; University of Southern California, USA; Occidental College, USA)
Supplementary Material
Abstract: In open software development environment, a large number of feature requests with mixed quality are often posted by stakeholders and usually managed in issue tracking systems. Thoroughly understanding and analyzing the real intents that feature requests imply is a labor-intensive and challenging task. In this paper, we introduce an approach to understand feature requests automatically. We generate a set of fuzzy rules based on natural language processing techniques that classify each sentence in feature requests into a set of categories: Intent, Explanation, Benefit, Drawback, Example and Trivia. Consequently, the feature requests can be automatically structured based on the classification results. We conduct experiments on 2,112 sentences taken from 602 feature requests of nine popular open source projects. The results show that our method can reach a high performance on classifying sentences from feature requests. Moreover, when applying fuzzy rules on machine learning methods, the performance can be improved significantly.


Time stamp: 2019-09-16T08:06:21+02:00