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Model-driven engineering practices in industry

Hutchinson, John, Rouncefield, Mark, Whittle, Jon·2011·Proceedings of the 33rd International Conference on Software Engineering

Abstract

In this paper, we attempt to address the relative absence of empirical studies of model driven engineering through describing the practices of three commercial organizations as they adopted a model driven engineering approach to their software development. Using in-depth semi-structured interviewing we invited practitioners to reflect on their experiences and selected three to use as exemplars or case studies. In documenting some details of attempts to deploy model driven practices, we identify some 'lessons learned', in particular the importance of complex organizational, managerial and social factors - as opposed to simple technical factors - in the relative success, or failure, of the endeavour. As an example of organizational change management the successful deployment of model driven engineering appears to require: a progressive and iterative approach; transparent organizational commitment and motivation; integration with existing organizational processes and a clear business focus.

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Metadata

Authors
Hutchinson, J., Rouncefield, M., & Whittle, J.
Venue
Proceedings of the 33rd International Conference on Software Engineering
Publisher
ACM
Published
2011-05-21
Pages
633-642
ISSN
0270-5257 (ISSN-L)
OpenAlex
W2095741627
Cited by
213
References
0
FWCI
15.10
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Model-driven engineering practices in industry. https://app.cochat.ai/papers/10.1145/1985793.1985882
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Hutchinson, J., Rouncefield, M., & Whittle, J. (2011). Model-driven engineering practices in industry. Proceedings of the 33rd International Conference on Software Engineering. https://doi.org/10.1145/1985793.1985882

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.