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Conference Papers Year : 2021

SEALS: A framework for building Self-Adaptive Virtual Machines


Over recent years, self-adaptation has become a major concern for software systems that evolve in changing environments. While expert developers may choose a manual implementation when self-adaptation is the primary concern, selfadaptation should be abstracted for non-expert developers or when it is a secondary concern. We present SEALS, a framework for building self-adaptive virtual machines for domainspecific languages. This framework provides first-class entities for the language engineer to promote domain-specific feedback loops in the definition of the DSL operational semantics. In particular, the framework supports the definition of (i) the abstract syntax and the semantics of the language as well as the correctness envelope defining the acceptable semantics for a domain concept, (ii) the feedback loop and associated trade-off reasoning, and (iii) the adaptations and the predictive model of their impact on the trade-off. We use this framework to build three languages with self-adaptive virtual machines and discuss the relevance of the abstractions, effectiveness of correctness envelopes, and compare their code size and performance results to their manually implemented counterparts. We show that the framework provides suitable abstractions for the implementation of self-adaptive operational semantics while introducing little performance overhead compared to a manual implementation.
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hal-03355253 , version 1 (27-09-2021)



Gwendal Jouneaux, Olivier Barais, Benoit Combemale, Gunter Mussbacher. SEALS: A framework for building Self-Adaptive Virtual Machines. SLE 2021 - 14th ACM SIGPLAN International Conference on Software Language Engineering, Oct 2021, Chicago, United States. pp.1-14, ⟨10.1145/3486608.3486912⟩. ⟨hal-03355253⟩
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