Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of 2018
DOI: 10.1145/3236024.3236064
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Parameterized model counting for string and numeric constraints

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Cited by 24 publications
(13 citation statements)
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“…The experimental results show that our approach outperforms fuzzing and is also effective in finding bugs in other types of solvers, as we demonstrate on the automata-based solver MT-ABC [11].…”
Section: Experimental Evaluationmentioning
confidence: 62%
See 2 more Smart Citations
“…The experimental results show that our approach outperforms fuzzing and is also effective in finding bugs in other types of solvers, as we demonstrate on the automata-based solver MT-ABC [11].…”
Section: Experimental Evaluationmentioning
confidence: 62%
“…Our technique is also not specific to SMT solvers. Since it treats the solver under test as a black box, it can be also applied to test the soundness and precision of other solvers, for instance, automatabased solvers, like SMC [26], ABC [10], and MT-ABC [11]. These solvers encode the input constraints as finite automata and determine their satisfiability by counting the number of possible models.…”
Section: Generating Unsatisfiable Formulasmentioning
confidence: 99%
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“…The constraint language for ABC supports all numeric constraints solved by off the shelf constraints solvers as well as typical string operations such as charAt, length, indexOf, substring, begins, concat, <, =, etc. Given a constraint C, ABC constructs a multi-track deterministic finite automaton (DFA) A C that characterizes all solutions for the constraint C, where L(A C ) corresponds to the set of solutions for C. For each string term γ or integer term β in the constraint grammar [4], ABC implements an automata constructor function which generates an automaton A that encodes the set of satisfying solutions for the term. Note that variables within string terms and integer terms appear in separate automata, as separate encodings are used for each (ASCII for strings, binary encoding for integers).…”
Section: Automata-based Constraint Solving and Model Countingmentioning
confidence: 99%
“…information flow analysis [16,23,29,31], Bayesian inference [8,11,26], and compiler optimization [25]. Originally stated with respect to Boolean formulas [5], more recent advances in model counting have extended counting capabilities to the theories of linear integer arithmetic [19,30], non-linear numeric constraints [6], strings [20,27,28], word-level counting for bit-vectors applied to the problem of automatic inference [7], and more recent work has begun to combine theories of strings and integers [2]. This paper presents the first tool with the capability of performing model counting directly on formulas with symbolic arrays.…”
mentioning
confidence: 99%