Synergy

Gulavani, Bhargav S.; Henzinger, Thomas A.; Kannan, Yamini; Nori, Aditya V.; Rajamani, Sriram K.

doi:10.1145/1181775.1181790

Cited by 166 publications

(13 citation statements)

References 29 publications

(54 reference statements)

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“…Various combinations have been proposed for verification [2,10,11,14,25,27,[29][30][31]35,37,40,50,64] and test-suite generation [1,32,34,36,38,47,51,54,56,59,60,63]. We focus on combinations that interleave approaches.…”

Section: Related Workmentioning

confidence: 99%

CoVeriTest: Cooperative Verifier-Based Testing

Beyer

Jakobs

2019

Lecture Notes in Computer Science

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Testing is a widely used method to assess software quality. Coverage criteria and coverage measurements are used to ensure that the constructed test suites adequately test the given software. Since manually developing such test suites is too expensive in practice, various automatic test-generation approaches were proposed. Since all approaches come with different strengths, combinations are necessary in order to achieve stronger tools. We study cooperative combinations of verification approaches for test generation, with high-level information exchange. We present CoVeriTest, a hybrid approach for test-case generation, which iteratively applies different conditional model checkers. Thereby, it allows to adjust the level of cooperation and to assign individual time budgets per verifier. In our experiments, we combine explicit-state model checking and predicate abstraction (from CPAchecker) to systematically study different CoVeriTest configurations. Moreover, CoVeriTest achieves higher coverage than state-of-the-art test-generation tools for some programs.

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Section: Related Workmentioning

confidence: 99%

CoVeriTest: Cooperative Verifier-Based Testing

Beyer

Jakobs

2019

Lecture Notes in Computer Science

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“…We do not require membership queries. Other techniques that use concrete data to guide verification include [22,3,24,39].…”

Section: Related Workmentioning

confidence: 99%

From invariant checking to invariant inference using randomized search

Sharma

Aiken

2016

Form Methods Syst Des

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Abstract. We describe a general framework c2i for generating an invariant inference procedure from an invariant checking procedure. Given a checker and a language of possible invariants, c2i generates an inference procedure that iteratively invokes two phases. The search phase uses randomized search to discover candidate invariants and the validate phase uses the checker to either prove or refute that the candidate is an actual invariant. To demonstrate the applicability of c2i, we use it to generate inference procedures that prove safety properties of numerical programs, prove non-termination of numerical programs, prove functional specifications of array manipulating programs, prove safety properties of string manipulating programs, and prove functional specifications of heap manipulating programs that use linked list data structures.

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“…Lee-Yannakakis partition refinement algorithm [42] does not work well on fig6; Synergy [31] fails to terminate on fig9; prog2 has a loop with a large constant number of iterations and predicate abstraction based tools like Slam take time proportional to the number of loop iterations. The program prog3 requires a disjunctive invariant.…”

Section: Programmentioning

confidence: 99%

“…Predicate abstraction based tools are geared towards computing arbitrary boolean combinations of predicates [6,9,31,1,10,30]. Among these, Yogi [31] uses test cases to determine where to refine its abstraction.…”

Section: Comparison With Linear Invariant Generationmentioning

confidence: 99%

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Verification as Learning Geometric Concepts

et al. 2013

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Abstract. We formalize the problem of program verification as a learning problem, showing that invariants in program verification can be regarded as geometric concepts in machine learning. Safety properties define bad states: states a program should not reach. Program verification explains why a program's set of reachable states is disjoint from the set of bad states. In Hoare Logic, these explanations are predicates that form inductive assertions. Using samples for reachable and bad states and by applying well known machine learning algorithms for classification, we are able to generate inductive assertions. By relaxing the search for an exact proof to classifiers, we obtain complexity theoretic improvements. Further, we extend the learning algorithm to obtain a sound procedure that can generate proofs containing invariants that are arbitrary boolean combinations of polynomial inequalities. We have evaluated our approach on a number of challenging benchmarks and the results are promising.

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Synergy

Cited by 166 publications

References 29 publications

CoVeriTest: Cooperative Verifier-Based Testing

CoVeriTest: Cooperative Verifier-Based Testing

From invariant checking to invariant inference using randomized search

Verification as Learning Geometric Concepts

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