Conceptualising, extracting and analysing requirements arguments in users' forums: The CrowdRE‐Arg framework. Issue 12 (27th August 2020)

• Record Type: Journal Article
• Authors: Ali Khan, Javed; Liu, Lin; Wen, Lijie; Ali, Raian
• Is Part Of: Journal of software. Volume 32:Issue 12(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-08-27
• Subjects: argumentation -- machine learning -- natural language processing -- new features -- requirements -- user forum; Software engineering -- Periodicals; Computer software -- Development -- Periodicals; Software maintenance -- Periodicals; 005.1
• DOI: 10.1002/smr.2309
• Languages: English
• ISSNs: 2047-7473

Abstract

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.