In model-driven development of safety-critical systems (like automotive, avionics or railways), wellformedness of models is repeatedly validated in order to detect design flaws as early as possible. Validation rules are often implemented by a large amount of imperative model traversal code which makes those rule implementations complicated and hard to maintain. Additionally as models are rapidly increasing in size and complexity, efficient execution of these operations is challenging for the currently available toolchains. However, checking well-formedness constraints can be interpreted as evaluation of model queries, and the operations as model transformations, where the validation task can be specified in a concise way, and executed efficiently. This paper presents a benchmark case and an evaluation framework to systematically assess the scalability of validating and revalidating well-formedness constraints over large models. The benchmark case defines a typical well-formedness validation scenario in the railway domain including the metamodel, an instance model generator, and a set of well-formedness constraints captured by queries and repair operations (imitating the work of systems engineers by model transformations). The benchmark case focuses on the execution time of the query evaluations with a special emphasis on reevaluations, as well as simple repair transformations.
['Gábor Szárnyas', 'Oszkár Semeráth', 'István Ráth', 'Dániel Varró']
The TTC 2015 train benchmark case for incremental model validation