Our paper “Towards an automated fault localizer while designing meta-models” has been accepted for publication in MDEbug, the Second International Workshop on Debugging in Model-Driven Engineering, organized as part of the ACM/IEEE 21st International Conference on Model Driven Engineering Languages and Systems (MODELS 2018).
Meta-models are the centrepiece of Model Driven Engineering, required in many activities: modelling, creating DSLs (Domain Specific Languages), DSLs (executable DSLs), or writing model transformations. Designing large meta-models could be a complicated and error-prone task. Meta-models should then be validated considering their instantiability in particular. Automatic model generators are used and if they are unable to generate models it means the metamodel with its instantiation parameters (e.g. size of the models) is wrong. Several generators exist, but most of them have binary output: success or failure, without helping the meta-model debugging. In this paper, we introduce an approach, in which we statically analyse a meta-model with its instantiation parameters. In this first work, we detect inconsistencies considering each reference or each inheritance separately. Therefore we provide feedback to the meta-model designer to help her to debug the meta-model.
Bibtex & Pre-print version
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