Mining Reddit Data to Elicit Students' Requirements During COVID-19 Pandemic
Shadikur Rahman, Faiz Ahmed, Maleknaz Nayebi

TL;DR
This paper explores mining Reddit discussions to identify students' requirements during COVID-19, proposing a novel focus on problem-related feedback rather than software-specific comments, and demonstrating promising NLP results.
Contribution
It introduces a new approach to requirements elicitation by analyzing problem-focused communication on social media, validated through a case study with machine learning techniques.
Findings
Achieved an F-score of 0.79 with Naive Bayes and TF-IDF.
Mining problem-related requirements from communication is feasible.
Preliminary results support the potential of social media for requirements gathering.
Abstract
Data-driven requirements engineering leverages the abundance of openly accessible and crowdsourced information on the web. By incorporating user feedback provided about a software product, such as reviews in mobile app stores, these approaches facilitate the identification of issues, bug fixes, and implementation of change requests. However, relying solely on user feedback about a software product limits the possibility of eliciting all requirements, as users may not always have a clear understanding of their exact needs from the software, despite their wealth of experience with the problem, event, or challenges they encounter and use the software to assist them. In this study, we propose a shift in requirements elicitation, focusing on gathering feedback related to the problem itself rather than relying solely on feedback about the software product. We conducted a case study on student…
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Taxonomy
TopicsSoftware Engineering Techniques and Practices · Software Engineering Research · Software System Performance and Reliability
