Journal of Software: Evolution and Process 2020

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.