Towards Artefact-based Requirements Engineering for Data-Centric Systems

• Many modern software-intensive systems employ artificial intelligence / machine-learning (AI/ML) components and are, thus, inherently data-centric. The behaviour of such systems depends on typically large amounts of data processed at run-Time rendering such non-deterministic systems as complex. This complexity growth affects our understanding on needs and practices in Requirements Engineering (RE). There is, however, still little guidance on how to handle requirements for such systems effectively: What are, for example, typical quality requirements classes What modelling concepts do we rely on or which levels of abstraction do we need to consider In fact, how to integrate such concepts into approaches for a more traditional RE still needs profound investigations. In this research preview paper, we report on ongoing efforts to establish an artefact-based RE approach for the development of datacentric systems (DCSs). To this end, we sketch a DCS development process with the newly proposed requirements categories and data-centric artefacts and briefly report on an ongoing investigation of current RE challenges in industry developing data-centric systems. © 2021 CEUR-WS. All rights reserved.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Programvaruteknik (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Software Engineering (hsv//eng)

Nyckelord

Artefact Orientation

Artificial Intelligence

Requirements Engineering

Computer software selection and evaluation

Complexity growth

Development process

Large amounts of data

Levels of abstraction

Nondeterministic systems

Quality requirements

Re challenges

Software intensive systems

Publikations- och innehållstyp

ref (ämneskategori)

kon (ämneskategori)