Continuous Model Validation using Reference Attribute Grammars

Johannes Mey, René Schöne, Görel Hedin, Emma Söderberg, Thomas Kühn, Niklas Fors, Jesper Öqvist, Uwe Aßmann

Forskningsoutput: Kapitel i bok/rapport/Conference proceedingKonferenspaper i proceedingPeer review

836 Nedladdningar (Pure)

Sammanfattning

Just like current software systems, models are characterised by increasing complexity and rate of change. Yet, these models only become useful if they can be continuously evaluated and validated. To achieve sufficiently low response times for large models, incremental analysis is required. Reference Attribute Grammars (RAGs) offer mechanisms to perform an incremental analysis efficiently using dynamic dependency tracking. However, not all features used in conceptual modelling are directly available in RAGs. In particular, support for non-containment model relations is only available through manual implementation. We present an approach to directly model uni- and bidirectional non-containment relations in RAGs and provide efficient means for navigating and editing them. 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.
Originalspråkengelska
Titel på värdpublikationProceedings of the 11th ACM SIGPLAN International Conference on Software Language Engineering (SLE ’18)
FörlagAssociation for Computing Machinery (ACM)
Sidor70-82
Antal sidor13
ISBN (elektroniskt)978-1-4503-6029-6
DOI
StatusPublished - 2018 nov.
EvenemangThe ACM SIGPLAN International Conference on Software Language Engineering - Boston, USA
Varaktighet: 2018 nov. 52018 nov. 6
Konferensnummer: 11
https://conf.researchr.org/track/sle-2018/

Konferens

KonferensThe ACM SIGPLAN International Conference on Software Language Engineering
Förkortad titelSLE
Land/TerritoriumUSA
OrtBoston
Period2018/11/052018/11/06
Internetadress

Ämnesklassifikation (UKÄ)

  • Datavetenskap (datalogi)

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