The project LEA-xDSML (Language Engineering for Analyzable Executable Domain-Specific Modeling Languages) resides in the context of Model-Driven Engineering (MDE), which proposes the use of domain-specific modeling languages (DSMLs) to reduce the complexity.
The project LEA-xDSML (Language Engineering for Analyzable Executable Domain-Specific Modeling Languages) resides in the context of Model-Driven Engineering (MDE), which proposes the use of domain-specific modeling languages (DSMLs) to reduce the complexity associated with the development of complex software-intensive systems, as, for instance, found in the automation domain, production domain, and automotive domain.
DSMLs are increasingly being developed to continuously leverage the domain-specific expertise of the various stakeholders involved in the development of complex system. Thereby, the integration of domain-specific knowledge into DSMLs can significantly improve the productivity of the development process and the quality of the final system. However, the development of DSMLs has also been recognized as a challenging and significant software engineering task itself.
In this project, we focus on the challenges associated with the development of executable DSMLs (xDSMLs) that support the modeling and analysis of complex system behaviors through model execution. In particular, we aim at overcoming the following three challenges: the lack of foundations for formalizing xDSMLs in a way that allows for model-level analyses; the high efforts associated with the development of domain-specific analysis tools for xDSMLs; and the lack of fault localization techniques for efficiently identifying faults in models defined with xDSMLs.
To overcome these challenges, the aim of this project is to develop a novel engineering framework for xDSMLs that will provide (i) concepts, techniques and processes to formalize xDSMLs usable for model-level behavior analyses, (ii) automation techniques for efficiently developing domain-specific model analysis tools for xDSMLs, (iii) and fault localization mechanisms for xDSMLs that allow an efficient debugging of models.
The framework will be iteratively developed and evaluated. The methodology for evaluating the framework builds on three major pillars, namely case studies, experiments with our master students, and collaborative studies with international collaborators.