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Conference Papers Year : 2023

Adaptive Structural Operational Semantics


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 selfadaptable behavior. We demonstrate, for RobLANG, that our approach provides suitable abstractions for specifying sound adaptive operational semantics while being more efficient.
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hal-04252577 , version 1 (20-10-2023)





Gwendal Jouneaux, Damian Frölich, Olivier Barais, Benoit Combemale, Gurvan Le Guernic, et al.. Adaptive Structural Operational Semantics. SLE 2023 - 16th ACM SIGPLAN International Conference on Software Language Engineering, Oct 2023, Cascais, Portugal. pp.29-42, ⟨10.1145/3623476.3623517⟩. ⟨hal-04252577⟩
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