1 research outputs found
The Train Benchmark: cross-technology performance evaluation of continuous model queries
In model-driven development of safety-critical
systems (like automotive, avionics or railways), well-
formedness of models is repeatedly validated in order to
detect design flaws as early as possible. In many indus-
trial tools, validation rules are still 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 increas-
ing in size and complexity, efficient execution of validation rules is challenging for the currently available tools.
Checking well-formedness constraints can be captured by
declarative queries over graph models, while model update
operations can be specified as model transformations. This
paper presents a benchmark for systematically assessing the
scalability of validating and revalidating well-formedness
constraints over large graph models. The benchmark defines
well-formedness validation scenarios in the railway domain:
a metamodel, an instance model generator and a set of well-
formedness constraints captured by queries, fault injection
and repair operations (imitating the work of systems engi-
neers by model transformations). The benchmark focuses
on the performance of query evaluation, i.e. its execution
time and memory consumption, with a particular empha-
sis on reevaluation. We demonstrate that the benchmark
can be adopted to various technologies and query engines,
including modeling tools; relational, graph and semantic
databases. The Train Benchmark is available as an open-
source project with continuous builds from
https://github.
com/FTSRG/trainbenchmark