Titlebook: Computer Aided Verification; 35th International C Constantin Enea,Akash Lal Conference proceedings‘‘‘‘‘‘‘‘ 2023 The Editor(s) (if applicabl

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Literaturdienst zur Handelsforschung 1993ntal SAT solving. We evaluate our approach on a broad set of benchmarks. On the logical fragment that our tool supports, it is competitive with state-of-the-art solvers. Our experiments also demonstrate that an eager SAT-based approach complements existing approaches to string solving in this specific fragment.

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https://doi.org/10.1007/978-3-658-18738-5easingly precise under- and overapproximations of the quantified sets, based on fixpoint iteration and automata learning. We report on encouraging experimental results with our model-checking algorithm, which we implemented in the tool?..

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Solving String Constraints Using SATntal SAT solving. We evaluate our approach on a broad set of benchmarks. On the logical fragment that our tool supports, it is competitive with state-of-the-art solvers. Our experiments also demonstrate that an eager SAT-based approach complements existing approaches to string solving in this specific fragment.

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https://doi.org/10.1007/978-3-663-12998-1hms, as well as efficiency with tight integration with the underlying SMT solver. This paper describes the architecture of . and its back-end engines, which include our recently introduced model-checking algorithm TPA for deep exploration. The description is complemented by extensive evaluation, demonstrating the competitive nature of the solver.

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0302-9743 hecking; neural networks and machine learning; ..Part II: Probabilistic systems; security and quantum systems; software verification..978-3-031-37702-0978-3-031-37703-7Series ISSN 0302-9743 Series E-ISSN 1611-3349

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https://doi.org/10.1007/978-3-8350-9220-4he notion of adjoints. In the absence of adjoints, one can use the second algorithm, which exploits lower sets and their principals. As a notable example of application, we consider quantitative reachability problems for Markov Decision Processes.