Relational Reference Attribute Grammars: Improving Continuous Model Validation (Artefact)
| Main Authors: | Johannes Mey, Carl Mai, René Schöne, Görel Hedin, Emma Söderberg, Thomas Kühn, Niklas Fors, Jesper Öqvist, Uwe Aßmann |
|---|---|
| Format: | info software Journal |
| Bahasa: | eng |
| Terbitan: |
, 2020
|
| Subjects: | |
| Online Access: |
https://zenodo.org/record/3666664 |
Daftar Isi:
- This artefact contains the source code, measurement environment and measurement data of the evaluation of the paper with the same name. Abstract of the Publication Just like current software systems, conceptual models are characterised by increasing complexity and rate of change. Yet, these models only become useful if they can be continuously evaluated, validated and serialized. To achieve sufficiently low response times for large models, incremental analysis is required. Reference Attribute Grammars (RAGs) offer mechanisms to perform incremental analysis efficiently using dynamic dependency tracking. However, not all features used in conceptual modelling are directly available in RAGs. In particular, support for noncontainment model relations is only available through encodings. We present an approach called Relational RAGs to directly model uni- and bidirectional noncontainment relations in RAGs and provide efficient means for navigating and editing them. Furthermore, we discuss the efficient and inter-operable serialization and deserialization of such model instances. This approach is evaluated using a scalable benchmark for incremental model editing and the JastAdd RAG system. Our work demonstrates the suitability of RAGs for validating complex and continuously changing models of current software systems.
- This work is partly supported by the German Research Foundation (DFG) in the SFB 912 HAEC, the project RISCOS, the Cluster of Excellence EXC 2050/1 CeTI and within the Research Training Group RoSI (GRK 1907), and by the German Federal Ministry of Education and Research within the project OpenLicht. This work is also partly supported by the Swedish Governmental Agency for Innovation Systems (VINNOVA) in the PIIA project 2017-02371 and by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation (KAW).