Valuating requirements arguments in the online user's forum for requirements decision-making: The CrowdRE-VArg framework

Javed Ali Khan, Affan Yasin, Rubia Fatima, Danish Vasan, Arif Ali Khan, Abdul Wahid Khan

Research output: Contribution to journalArticlepeer-review


User forums enable a large population of crowd-users to publicly share their experience, useful thoughts, and concerns about the software applications in the form of user reviews. Recent research studies have revealed that end-user reviews contain rich and pivotal sources of information for the software vendors and developers that can help undertake software evolution and maintenance tasks. However, such user-generated information is often fragmented, with multiple viewpoints from various stakeholders involved in the ongoing discussions in the Reddit forum. In this article, we proposed a crowd-based requirements engineering by valuation argumentation (CrowdRE-VArg) approach that analyzes the end-users discussion in the Reddit forum and identifies conflict-free new features, design alternatives, or issues, and reach a rationale-based requirements decision by gradually valuating the relative strength of their supporting and attacking arguments. The proposed approach helps to negotiate the conflict over the new features or issues between the different crowd-users on the run by finding a settlement that satisfies the involved crowd-users in the ongoing discussion in the Reddit forum using argumentation theory. For this purpose, we adopted the bipolar gradual valuation argumentation framework, extended from the abstract argumentation framework and abstract valuation framework. The automated CrowdRE-VArg approach is illustrated through a sample crowd-users conversation topic adopted from the Reddit forum about Google Map mobile application. Finally, we applied natural language processing and different machine learning algorithms to support the automated execution of the CrowdRE-VArg approach. The results demonstrate that the proposed CrowdRE-VArg approach works as a proof-of-concept and automatically identifies prioritized requirements-related information for software engineers.

Original languageEnglish
Pages (from-to)2537-2573
Number of pages37
JournalSoftware - Practice and Experience
Issue number12
Publication statusPublished - Dec 2022
Externally publishedYes


  • argumentation
  • machine learning
  • natural language processing
  • new features
  • Reddit forum
  • requirements


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