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Understanding feature requests by leveraging fuzzy method and linguistic analysis

Authors:

Barry BoehmAuthors Info & Claims

ASE '17: Proceedings of the 32nd IEEE/ACM International Conference on Automated Software Engineering

Pages 440 - 450

Published: 30 October 2017 Publication History

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.

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Cited By

Zhao YXiao LWong S(2024)A Platform-Agnostic Framework for Automatically Identifying Performance Issue Reports With Heuristic Linguistic PatternsIEEE Transactions on Software Engineering10.1109/TSE.2024.339062350:7(1704-1725)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1109/TSE.2024.3390623
Veizaga AShin SBriand L(2024)Automated Smell Detection and Recommendation in Natural Language RequirementsIEEE Transactions on Software Engineering10.1109/TSE.2024.336103350:4(695-720)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1109/TSE.2024.3361033
Zhao YXiao LBabvey PSun LWong SMartinez AWang XDevanbu PCohen MZimmermann T(2020)Automatically identifying performance issue reports with heuristic linguistic patternsProceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3368089.3409674(964-975)Online publication date: 8-Nov-2020
https://dl.acm.org/doi/10.1145/3368089.3409674

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Information & Contributors

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Published In

cover image Guide Proceedings

ASE '17: Proceedings of the 32nd IEEE/ACM International Conference on Automated Software Engineering

October 2017

1033 pages

ISBN:9781538626849

General Chair:
Grigore Rosu
University of Illinois at Urbana-Champaign, USA
,
Program Chairs:
Massimiliano Di Penta
University of Sannio, Italy
,
Tien N. Nguyen
University of Texas at Dallas, USA

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IEEE Press

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Published: 30 October 2017

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Cited By

Zhao YXiao LWong S(2024)A Platform-Agnostic Framework for Automatically Identifying Performance Issue Reports With Heuristic Linguistic PatternsIEEE Transactions on Software Engineering10.1109/TSE.2024.339062350:7(1704-1725)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1109/TSE.2024.3390623
Veizaga AShin SBriand L(2024)Automated Smell Detection and Recommendation in Natural Language RequirementsIEEE Transactions on Software Engineering10.1109/TSE.2024.336103350:4(695-720)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1109/TSE.2024.3361033
Zhao YXiao LBabvey PSun LWong SMartinez AWang XDevanbu PCohen MZimmermann T(2020)Automatically identifying performance issue reports with heuristic linguistic patternsProceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3368089.3409674(964-975)Online publication date: 8-Nov-2020
https://dl.acm.org/doi/10.1145/3368089.3409674

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