Adaptive Structural Operational Semantics
| Authors |
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|---|---|
| Publication date | 2023 |
| Host editors |
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| 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) |
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| 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 |
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| 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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