About the project
Gotten builds domain-specific metamorphic testing environments from model-driven artefacts.
The framework combines meta-models, DSLs, and domain processors to make MT definitions, execution, reporting, and follow-up test generation more systematic and reusable.
Core capabilities
- Define input, output, and processor features with mrDSL.
- Specify metamorphic relations over domain concepts and executions.
- Generate follow-up test cases with search-based strategies in fowDSL.
- Apply the same framework to multiple domains while keeping domain-specific models.
Current examples
- Cloud simulators with relations over CPU, network, memory, energy, and time.
- Video streaming APIs with relations over searches, updates, and result sets.
- Autonomous vehicles with relations over nominal speed, the number of obstacles, and the sequence of reference waypoints.
- Finite automata with relations over automaton models and word models representing the strings to be processed.
- Public repositories and downloadable sample projects for these domains.
Authors and contributors
Gotten has been developed by Pablo Gómez-Abajo, Pablo C. Cañizares, Alberto Núñez, Esther Guerra and Juan de Lara, with technical foundations that build on tools such as Xtext, Henshin, and MOMoT.
Related publications
- 3
Gómez-Abajo, P., Cañizares, P. C., Núñez, A., Guerra, E., de Lara, J. Gotten: A model-driven solution to engineer domain-specific metamorphic testing environments, 2023. ACM/IEEE 26th International Conference on Model Driven Engineering Languages and Systems (MoDELS 2023), Västerås. Tool demo. - 2 Gómez-Abajo, P., Cañizares, P. C., Núñez, A., Guerra, E., de Lara, J. Automated engineering of domain-specific metamorphic testing environments, 2023. In Information and Software Technology, Elsevier.
- 1 Cañizares, P. C., Gómez-Abajo, P., Núñez, A., Guerra, E., de Lara, J. New ideas: Automated engineering of metamorphic testing environments for domain-specific languages, 2021. In ACM SIGPLAN International Conference on Software Language Engineering (SLE 2021), Chicago. Best new ideas/vision paper award at SLE'21
Acknowledgements
This work has been funded by the Spanish Ministry of Science (RTI2018-095255-B-I00, project “MASSIVE”) and the R&D programme of Madrid (P2018/TCS-4314, project “FORTE”).