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Cited by 374

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Empirical assessment of MDE in industry

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

Abstract

This paper presents some initial results from a twelve-month empirical research study of model driven engineering (MDE). Using largely qualitative questionnaire and interview methods we investigate and document a range of technical, organizational and social factors that apparently influence organizational responses to MDE: specifically, its perception as a successful or unsuccessful organizational intervention. We then outline a range of lessons learned. Whilst, as with all qualitative research, these lessons should be interpreted with care, they should also be seen as providing a greater understanding of MDE practice in industry, as well as shedding light on the varied, and occasionally surprising, social, technical and organizational factors that affect success and failure. We conclude by suggesting how the next phase of the research will attempt to investigate some of these issues from a different angle and in greater depth.

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Metadata

Authors
Hutchinson, J., Whittle, J., Rouncefield, M., & Kristoffersen, S.
Venue
Proceedings of the 33rd International Conference on Software Engineering
Publisher
ACM
Published
2011-05-21
Pages
471-480
ISSN
0270-5257 (ISSN-L)
OpenAlex
W2167736435
Cited by
374
References
2
FWCI
24.16
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Empirical assessment of MDE in industry. https://app.cochat.ai/papers/10.1145/1985793.1985858
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Hutchinson, J., Whittle, J., Rouncefield, M., & Kristoffersen, S. (2011). Empirical assessment of MDE in industry. Proceedings of the 33rd International Conference on Software Engineering. https://doi.org/10.1145/1985793.1985858

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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.