Conceptualising, extracting and analysing requirements arguments in users' forums: The CrowdRE-Arg framework. Ali Khan, J., Liu, L., Wen, L., & Ali, R. Journal of Software: Evolution and Process, 2020. tex.author_keywords: argumentation; machine learning; natural language processing; new features; requirements; user forum tex.document_type: Article tex.source: Scopus![Conceptualising, extracting and analysing requirements arguments in users' forums: The CrowdRE-Arg framework [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Due to the pervasive use of online forums and social media, users' feedback are more accessible today and can be used within a requirements engineering context. However, such information is often fragmented, with multiple perspectives from multiple parties involved during on-going interactions. In this paper, the authors propose a Crowd-based Requirements Engineering approach by Argumentation (CrowdRE-Arg). The framework is based on the analysis of the textual conversations found in user forums, identification of features, issues and the arguments that are in favour or opposing a given requirements statement. The analysis is to generate an argumentation model of the involved user statements, retrieve the conflicting-viewpoints, reason about the winning-arguments and present that to systems analysts to make informed-requirements decisions. For this purpose, the authors adopted a bipolar argumentation framework and a coalition-based meta-argumentation framework as well as user voting techniques. The CrowdRE-Arg approach and its algorithms are illustrated through two sample conversations threads taken from the Reddit forum. Additionally, the authors devised algorithms that can identify conflict-free features or issues based on their supporting and attacking arguments. The authors tested these machine learning algorithms on a set of 3,051 user comments, preprocessed using the content analysis technique. The results show that the proposed algorithms correctly and efficiently identify conflict-free features and issues along with their winning arguments. © 2020 John Wiley & Sons, Ltd.
@article{AliKhan2020,
title = {Conceptualising, extracting and analysing requirements arguments in users' forums: {The} {CrowdRE}-{Arg} framework},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089977377&doi=10.1002%2fsmr.2309&partnerID=40&md5=f778be334eb68d79d3d8ea853e589e6a},
doi = {10.1002/smr.2309},
abstract = {Due to the pervasive use of online forums and social media, users' feedback are more accessible today and can be used within a requirements engineering context. However, such information is often fragmented, with multiple perspectives from multiple parties involved during on-going interactions. In this paper, the authors propose a Crowd-based Requirements Engineering approach by Argumentation (CrowdRE-Arg). The framework is based on the analysis of the textual conversations found in user forums, identification of features, issues and the arguments that are in favour or opposing a given requirements statement. The analysis is to generate an argumentation model of the involved user statements, retrieve the conflicting-viewpoints, reason about the winning-arguments and present that to systems analysts to make informed-requirements decisions. For this purpose, the authors adopted a bipolar argumentation framework and a coalition-based meta-argumentation framework as well as user voting techniques. The CrowdRE-Arg approach and its algorithms are illustrated through two sample conversations threads taken from the Reddit forum. Additionally, the authors devised algorithms that can identify conflict-free features or issues based on their supporting and attacking arguments. The authors tested these machine learning algorithms on a set of 3,051 user comments, preprocessed using the content analysis technique. The results show that the proposed algorithms correctly and efficiently identify conflict-free features and issues along with their winning arguments. © 2020 John Wiley \& Sons, Ltd.},
journal = {Journal of Software: Evolution and Process},
author = {Ali Khan, J. and Liu, L. and Wen, L. and Ali, R.},
year = {2020},
note = {tex.author\_keywords: argumentation; machine learning; natural language processing; new features; requirements; user forum
tex.document\_type: Article
tex.source: Scopus},
keywords = {\#nosource},
}
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