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A survey on model-based testing approaches: a systematic review

Dias Neto, Arilo C., Subramanyan, Rajesh, Vieira, Marlon, Travassos, Guilherme H.·2007·Proceedings of the 1st ACM international workshop on Empirical assessment of software engineering languages and technologies: held in conjunction with the 22nd IEEE/ACM International Conference on Automated Software Engineering (ASE) 2007

Abstract

This paper describes a systematic review performed on model-based testing (MBT) approaches. A selection criterion was used to narrow the initially identified four hundred and six papers to focus on seventy-eight papers. Detailed analysis of these papers shows where MBT approaches have been applied, the characteristics, and the limitations. The comparison criteria includes representation models, support tools, test coverage criteria, the level of automation, intermediate models, and the complexity. This paper defines and explains the review methodology and presents some results.

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Metadata

Authors
Dias Neto, A. C., Subramanyan, R., Vieira, M., & Travassos, G. H.
Venue
Proceedings of the 1st ACM international workshop on Empirical assessment of software engineering languages and technologies: held in conjunction with the 22nd IEEE/ACM International Conference on Automated Software Engineering (ASE) 2007
Publisher
ACM
Published
2007-11-05
Pages
31-36
OpenAlex
W2130422196
Cited by
306
References
0
FWCI
5.47
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: A survey on model-based testing approaches: a systematic review. https://app.cochat.ai/papers/10.1145/1353673.1353681
The paper
Dias Neto, A. C., Subramanyan, R., Vieira, M., & Travassos, G. H. (2007). A survey on model-based testing approaches: a systematic review. Proceedings of the 1st ACM international workshop on Empirical assessment of software engineering languages and technologies: held in conjunction with the 22nd IEEE/ACM International Conference on Automated Software Engineering (ASE) 2007. https://doi.org/10.1145/1353673.1353681

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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.