We provide a new perspective on the problem how high-level state machine models with abstract actions can be related to low-level models in which these actions are refined by sequences of concrete actions. We describe the connection between high-level and low-level actions using action codes, a variation of the prefix codes known from coding theory. For each action code ℛ, we introduce a contraction operator α_ℛ that turns a low-level model ℳ into a high-level model, and a refinement operator ϱ_ℛ that transforms a high-level model 𝒩 into a low-level model. We establish a Galois connection ϱ_ℛ(𝒩) ⊑ ℳ ⇔ 𝒩 ⊑ α_ℛ(ℳ), where ⊑ is the well-known simulation preorder. For conformance, we typically want to obtain an overapproximation of model ℳ. To this end, we also introduce a concretization operator γ_ℛ, which behaves like the refinement operator but adds arbitrary behavior at intermediate points, giving us a second Galois connection α_ℛ(ℳ) ⊑ 𝒩 ⇔ ℳ ⊑ γ_ℛ(𝒩). Action codes may be used to construct adaptors that translate between concrete and abstract actions during learning and testing of Mealy machines. If Mealy machine ℳ models a black-box system then α_ℛ(ℳ) describes the behavior that can be observed by a learner/tester that interacts with this system via an adaptor derived from code ℛ. Whenever α_ℛ(ℳ) implements (or conforms to) 𝒩, we may conclude that ℳ implements (or conforms to) γ_ℛ (𝒩). Almost all results, examples, and counter-examples are formalized in Coq.
@InProceedings{vaandrager_et_al:LIPIcs.ICALP.2023.137, author = {Vaandrager, Frits and Wi{\ss}mann, Thorsten}, title = {{Action Codes}}, booktitle = {50th International Colloquium on Automata, Languages, and Programming (ICALP 2023)}, pages = {137:1--137:20}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, ISBN = {978-3-95977-278-5}, ISSN = {1868-8969}, year = {2023}, volume = {261}, editor = {Etessami, Kousha and Feige, Uriel and Puppis, Gabriele}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2023.137}, URN = {urn:nbn:de:0030-drops-181895}, doi = {10.4230/LIPIcs.ICALP.2023.137}, annote = {Keywords: Automata, Models of Reactive Systems, LTS, Action Codes, Action Refinement, Action Contraction, Galois Connection, Model-Based Testing, Model Learning} }
Feedback for Dagstuhl Publishing