Synthesis and compositional verification using language learning
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Summary
A sound solution to automatically extract temporal specifications, which uses regular language learning and symbolic model checking and an automated solution for discovering assumptions based on the learning algorithm are proposed.
- Type
- article
- Published
- 2007-01-01
- Cited by
- 0
- References
- 105
- OpenAlex
- https://openalex.org/W18161554
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:93278
Keywords
Computer science, Correctness, Model checking, Programming language, Oracle
References
- UCPOP: A Sound, Complete, Partial Order Planner for ADL
- Reasoning about Actions and Planning in LTL Action Theories
- Planning with Incomplete Information as Heuristic Search in Belief Space
- A Framework for Planning with Extended Goals under Partial Observability
- Planning as Model Checking for Extended Goals in Non-deterministic Domains
- 2Planning for Contingencies: A Decision-based Approach
- TALplanner: A temporal logic based forward chaining planner
- Planning with Extended Goals and Partial Observability
- Planning for temporally extended goals
- Conditional progressive planning under uncertainty
- Black Box Checking
- Planning in Nondeterministic Domains under Partial Observability via Symbolic Model Checking
- Proof Rules for Automated Compositional Verification through Learning
- Modern Operating Systems
- The Temporal Semantics of Concurrent Programs
- Software model checking: extracting verification models from source code †
- Planning Control Rules for Reactive Agents
- Learning Regular Sets from Queries and Counterexamples
- Weak, strong, and strong cyclic planning via symbolic model checking
- Enforcing high-level protocols in low-level software
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