Adaptive Structural Operational Semantics

Authors
Publication date 2023
Host editors
  • J. Saraiva
  • T. Degueule
  • E. Scott
Book title Proceedings of the 16th ACM SIGPLAN International Conference on Software Language Engineering (SLE '23)
Book subtitle October 23-24, 2023, Cascais, Portugal
ISBN (electronic)
  • 9798400703966
Event 16th ACM SIGPLAN International Conference on Software Language Engineering, SLE 2023
Pages (from-to) 29-42
Number of pages 14
Publisher New York, NY: Association for Computing Machinery
Organisations
  • Faculty of Science (FNWI) - Informatics Institute (IVI)
Abstract

Software systems evolve more and more in complex and changing environments, often requiring runtime adaptation to best deliver their services. When self-adaptation is the main concern of the system, a manual implementation of the underlying feedback loop and trade-off analysis may be desirable. However, the required expertise and substantial development effort make such implementations prohibitively difficult when it is only a secondary concern for the given domain. In this paper, we present ASOS, a metalanguage abstracting the runtime adaptation concern of a given domain in the behavioral semantics of a domain-specific language (DSL), freeing the language user from implementing it from scratch for each system in the domain. We demonstrate our approach on RobLANG, a procedural DSL for robotics, where we abstract a recurrent energy-saving behavior depending on the context. We provide formal semantics for ASOS and pave the way for checking properties such as determinism, completeness, and termination of the resulting self-adaptable language. We provide first results on the performance of our approach compared to a manual implementation of this self-adaptable behavior. We demonstrate, for RobLANG, that our approach provides suitable abstractions for specifying sound adaptive operational semantics while being more efficient.

Document type Conference contribution
Language English
Published at https://doi.org/10.1145/3623476.3623517
Other links https://www.scopus.com/pages/publications/85177616853
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