Data-Driven Requirements Elicitation: A Systematic Literature Review

Abstract

Requirements engineering has traditionally been stakeholder-driven. In addition to domain knowledge, widespread digitalization has led to the generation of vast amounts of data (Big Data) from heterogeneous digital sources such as the Internet of Things (IoT), mobile devices, and social networks. The digital transformation has spawned new opportunities to consider such data as potentially valuable sources of requirements, although they are not intentionally created for requirements elicitation. A challenge to data-driven requirements engineering concerns the lack of methods to facilitate seamless and autonomous requirements elicitation from such dynamic and unintended digital sources. There are numerous challenges in processing the data effectively to be fully exploited in organizations. This article, thus, reviews the current state-of-the-art approaches to data-driven requirements elicitation from dynamic data sources and identifies research gaps. We obtained 1848 hits when searching six electronic databases. Through a two-level screening and a complementary forward and backward reference search, 68 papers were selected for final analysis. The results reveal that the existing automated requirements elicitation primarily focuses on utilizing human-sourced data, especially online reviews, as requirements sources, and supervised machine learning for data processing. The outcomes of automated requirements elicitation often result in mere identification and classification of requirements-related information or identification of features, without eliciting requirements in a ready-to-use form. This article highlights the need for developing methods to leverage process-mediated and machine-generated data for requirements elicitation and addressing the issues related to variety, velocity, and volume of Big Data for the efficient and effective software development and evolution.

Analysing app reviews for software engineering: a systematic literature review

“…In recent years, scholars have been also studying on-line user feedback from other digital sources such as microblogs e.g., Twitter (Guzman et al 2017), on-line forums e.g., Reddit (Khan et al 2019), or issue tracking systems e.g., JIRA (Nyamawe et al 2019). Most research efforts, however, have been focused on analyzing app reviews (Lim et al 2021). Supposedly, the large number of this data, their availability and their usefulness make app reviews unique and thus the most frequently studied type of on-line user feedback (Lim et al 2021).…”

“…Most research efforts, however, have been focused on analyzing app reviews (Lim et al 2021). Supposedly, the large number of this data, their availability and their usefulness make app reviews unique and thus the most frequently studied type of on-line user feedback (Lim et al 2021).…”

Improving RE-SWOT Analysis with Sentiment Classification: A Case Study of Travel Agencies

“…SWOT analysis can facilitate software requirements as well [34,35]. Recently, eliciting requirements from online user reviews has become a software engineering trend [36,37]. With the help of NLP, Groen et al [38] found that the automatic analysis of user requirements has better scalability than manual analysis.…”

Online User Review Analysis for Product Evaluation and Improvement

“…Recent studies have found that manufacturers can make use of online review data to make favorable decisions and gain competitive advantages [12,13]. And there are plenty of studies applying nature language techniques or machine learning techniques to mining user requirements from online reviews, such as review cleaning, information extraction, and sentiment analysis [14].…”