Abstract
Model-based testing involves testing a system under test for conformance to a model that specifies its behaviour. An important aspect for model-based testing is the implementation relation that defines precisely when a system under test conforms to its model. The implementation relation \({\textbf{ioco}} \) has often been used and studied in model-based testing when models are expressed as labelled transition systems, and there are tools implementing \({\textbf{ioco}} \)-based test generation. An alternative, slightly different implementation relation is \({\textbf{uioco}} \), which is more recent, has been less studied, and there are no tools for it. We will compare \({\textbf{ioco}} \) and \({\textbf{uioco}} \) on a couple of aspects, viz. intuition, the decision whether a test observation is correct, the definition of a consistent refinement relation, the construction of a canonical implementation for each specification, and the relation to other input-output implementation relations. For all these aspects, we conclude that \({\textbf{uioco}} \) is the preferred implementation relation, so, goodbye \({\textbf{ioco}} \), hello \({\textbf{uioco}} \).
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Acknowledgements
We thank Piërre van de Laar, Tim Willemse, and the anonymous reviewers for their valuable feedback and suggestions.
And, of course, we wish to thank Frits Vaandrager. Not only is this work dedicated to him, on the occasion of his 60th birthday, but Frits also played a major role in our analysis of the secrets of ioco and uioco, if not through his feedback and discussions on our research, then surely through the daily dose of optimism and the amusing conversations during the coffee breaks. We thank Frits for his inspiration, support, and pleasant collaboration, and for giving us quiescence. Without these this contribution would not have been written. Frits, thank you, and happy birthday!
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Tretmans, J., Janssen, R. (2022). Goodbye ioco. In: Jansen, N., Stoelinga, M., van den Bos, P. (eds) A Journey from Process Algebra via Timed Automata to Model Learning . Lecture Notes in Computer Science, vol 13560. Springer, Cham. https://doi.org/10.1007/978-3-031-15629-8_26
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