Data-Driven Requirements Elicitation: A Systematic Literature Review

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