Abstract
Validating and debugging conceptual models is a very time-consuming task. Though separate software tools for model validation and machine learning are available, their integration for an automated support of the debugging-validation process still needs to be explored. The synergy between model validation for finding intended/unintended conceptual models instances and machine learning for suggesting repairs promises to be a fruitful relationship. This paper provides a preliminary description of a framework for an adequate automatic support to engineers and domain experts in the proper design of a conceptual model. By means of a running example, the analysis will focus on two main aspects: i) the process by which formal, tool-supported methods can be effectively used to generate negative and positive examples, given an input conceptual model; ii) the key role of a learning system in uncovering error-prone structures and suggesting conceptual modeling repairs.
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Notes
- 1.
For a detailed analysis of model checking and model finding see [10].
- 2.
From now on we use the terms “simulation run” and “configuration” interchangeably, where a simulation run is the result of an interpretation function satisfying the conceptual model. In other words: if we take the UML diagram as a M1-model (in the MDA-sense), a configuration is a M0-model that could instantiate that M1-model; if we take the UML diagram as a logical specification, then a configuration is a logical model of that specification. Finding these valid configurations given a specification is the classical task performed by a model finder.
- 3.
This step may require a previous conversion step, from the language used to design the conceptual model (e.g. UML, OntoUML) to the model finder specifications as in, e.g., [4].
- 4.
Notice that Alloy produces ‘0’ and ‘1’ instances only, we numbered the instances considering the full list of possible configurations.
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Fumagalli, M., Sales, T.P., Guizzardi, G. (2020). Towards Automated Support for Conceptual Model Diagnosis and Repair. In: Grossmann, G., Ram, S. (eds) Advances in Conceptual Modeling. ER 2020. Lecture Notes in Computer Science(), vol 12584. Springer, Cham. https://doi.org/10.1007/978-3-030-65847-2_2
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