Holistic data-driven requirements elicitation in the big data era

Abstract

Digital transformation stimulates continuous generation of large amounts of digital data, both in organizations and in society at large. As a consequence, there have been growing efforts in the Requirements Engineering community to consider digital data as sources for requirements acquisition, in addition to human stakeholders. The volume, velocity and variety of the data make requirements discovery increasingly dynamic, but also unstructured and complex, which current elicitation methods are unable to consider and manage in a systematic and efficient manner. We propose a framework, in the form of a conceptual metamodel and a method, for continuous and automated acquisition, analysis and aggregation of heterogeneous digital sources that aims to support data-driven requirements elicitation and management. The usability of the framework is partially validated by an in-depth case study from the business sector of video game development.

From Data to Human-Readable Requirements: Advancing Requirements Elicitation through Language-Transformer-Enhanced Opportunity Mining

“…Requirements can generally be classified differently. The main types of requirements are functional requirements-requirements and tasks to be accomplished by a system, and quality requirements-also referred to as 'constraints' in literature-that describe the system's behavior [7].…”