SENov 30, 2016

Artefact-based Requirements Engineering: The AMDiRE Approach

arXiv:1611.10024v149 citations
Originality Synthesis-oriented
AI Analysis

This addresses the problem of implementing RE in complex domains by shifting focus from activities to artefacts, but it appears incremental as it builds on existing artefact-based paradigms.

The paper tackles the complexity of Requirements Engineering (RE) by proposing an artefact-based approach called AMDiRE, developed from six years of research and industrial evaluations, though it does not report specific numerical results.

The various influences in the processes and application domains make Requirements Engineering (RE) inherently complex and difficult to implement. In general, we have two options for establishing an RE approach: we can either establish an activity-based RE approach or we can establish an artefact-based one where project participants concentrate on the RE artefacts rather than on the way of creating them. While a number of activity-based RE approaches have been proposed in recent years, we have gained much empirical evidence and experiences about the advantages of the artefact-based paradigm for RE. However, artefact orientation is still a young paradigm with various interpretations and practical manifestations whereby we need a clear understanding of its basic concepts and a consolidated and evaluated view on the paradigm. In this article, we contribute an artefact-based approach to RE (AMDiRE) that emerges from six years of experiences in fundamental and evidence-based research. To this end, we first discuss the basic notion of artefact orientation and its evolution in recent years. We briefly introduce a set of artefact-based RE models we developed in industrial research cooperations for different application domains, show their empirical evaluations, and their dissemination into academia and practice, eventually leading to the AMDiRE approach. We conclude with a discussion of experiences we made during the development and different industrial evaluations, and lessons learnt.

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