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Relational reference attribute grammars: Improving continuous model validation

Mey, Johannes ; Schöne, René ; Hedin, Görel LU ; Söderberg, Emma LU ; Kühn, Thomas ; Fors, Niklas LU ; Öqvist, Jesper LU and Assmann, Uwe (2020) In Journal of Computer Languages 57.
Abstract
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... (More)
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. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Incremental model evaluation, Bidirectional relations, Reference attribute grammars
in
Journal of Computer Languages
volume
57
publisher
Elsevier
external identifiers
  • scopus:85079355622
ISSN
2590-1184
DOI
10.1016/j.cola.2019.100940
language
English
LU publication?
yes
id
ac913939-abe9-4fbb-b2c8-b2609bf40f48
date added to LUP
2020-02-21 10:38:00
date last changed
2020-05-24 06:30:09
@article{ac913939-abe9-4fbb-b2c8-b2609bf40f48,
  abstract     = {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.},
  author       = {Mey, Johannes and Schöne, René and Hedin, Görel and Söderberg, Emma and Kühn, Thomas and Fors, Niklas and Öqvist, Jesper and Assmann, Uwe},
  issn         = {2590-1184},
  language     = {eng},
  month        = {04},
  publisher    = {Elsevier},
  series       = {Journal of Computer Languages},
  title        = {Relational reference attribute grammars: Improving continuous model validation},
  url          = {http://dx.doi.org/10.1016/j.cola.2019.100940},
  doi          = {10.1016/j.cola.2019.100940},
  volume       = {57},
  year         = {2020},
}