Finding bugs with a constraint solver

JacksonDaniel,; VaziriMandana,

doi:10.1145/347636.383378

Cited by 63 publications

(89 citation statements)

References 20 publications

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“…The experiments show that our approach does work in practice for non-trivial data structures, and with time and space requirements which are as good as or better than those for the previous more specialized versions [25,14] and related approaches with similar goals [31,24,13,17].…”

Section: Implementation and Evaluationmentioning

confidence: 84%

“…The constraint solver Alloy has been used to verify properties about bounded initial segments of computation sequences [24]. While this is not a complete decision procedure even for straight-line code, it finds many errors and can produce counterexamples.…”

Section: Related Workmentioning

confidence: 99%

“…Such a counterexample is clearly useful for locating the bug. Notice that for this bug, the approach in [24] would not find the bug for heap bounds of less than four records.…”

Section: Implementation and Evaluationmentioning

confidence: 99%

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The Pointer Assertion Logic Engine

Möller¹,

Schwartzbach²

2000

BRICS

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We present a new framework for verifying partial specifications of programs in order to catch type and memory errors and check data structure invariants. Our technique can verify a large class of data structures, namely all those that can be expressed as graph types. Earlier versions were restricted to simple special cases such as lists or trees. Even so, our current implementation is as fast as the previous specialized tools.Programs are annotated with partial specifications expressed in Pointer Assertion Logic, a new notation for expressing properties of the program store. We work in the logical tradition by encoding the programs and partial specifications as formulas in monadic second-order logic. Validity of these formulas is checked by the MONA tool, which also can provide explicit counterexamples to invalid formulas.Other work with similar goals is based on more traditional program analyses, such as shape analysis. That approach requires explicit introduction of an appropriate operational semantics along with a proof of correctness whenever a new data structure is being considered. In comparison, our approach only requires the data structure to be abstractly described in Pointer Assertion Logic.

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Section: Implementation and Evaluationmentioning

confidence: 84%

Section: Related Workmentioning

confidence: 99%

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The Pointer Assertion Logic Engine

Möller¹,

Schwartzbach²

2000

BRICS

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show abstract

“…However, many large instances of SAT generated from real life problems can be successfully solved by heuristic SAT solvers. For example, SAT solvers find application in AI planning [2], circuit testing [3], software verification [4], microprocessor verification [5], model checking [6], etc. This has motivated research in efficient heuristic SAT solvers.…”

Section: Introductionmentioning

confidence: 99%

“…Stochastic methods are likely to be adopted in AI planning [2] and FPGA routing [13], where instances are likely to be satisfiable and proving unsatisfiability is not required. However, for many other domains, particularly some verification problems [4,6], the primary task is to prove unsatisfiability of the instances. Hence, complete SAT solvers are required in these cases.…”

Section: Introductionmentioning

confidence: 99%

Zchaff2004: An Efficient SAT Solver

Mahajan

Malik

2005

Theory and Applications of Satisfiability Testing

110

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Abstract. The Boolean Satisfiability Problem (SAT) is a well known NP-Complete problem. While its complexity remains a source of many interesting questions for theoretical computer scientists, the problem has found many practical applications in recent years. The emergence of efficient SAT solvers which can handle large structured SAT instances has enabled the use of SAT solvers in diverse domains such as electronic design automation and artificial intelligence. These applications continue to motivate the development of faster and more robust SAT solvers. In this paper, we describe the popular SAT solver zchaff with a focus on recent developments.

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The GridSAT portal: a Grid Web‐based portal for solving satisfiability problems using the national cyberinfrastructure

Chrabakh

Wolski

2006

Concurrency and Computation

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SUMMARYWe present a Grid portal problem (which is accessible through http://orca.cs.ucsb.edu/sat portal) for solving Boolean satisfiability. The portal provides a simple and public interface to a sophisticated and complex Grid application-GridSAT-running on a large set of distributed computational resources hosted in different large-scale national computing centers (i.e. the national cyberinfrastructure circa 2005). In this paper we describe the design goals of the portal and how it has influenced some of the application features. We also describe how the adaptive and self-tuning features of Grid applications (written from first principles) make the portal simpler and easier to implement.

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Finding bugs with a constraint solver

Cited by 63 publications

References 20 publications

The Pointer Assertion Logic Engine

The Pointer Assertion Logic Engine

Zchaff2004: An Efficient SAT Solver

The GridSAT portal: a Grid Web‐based portal for solving satisfiability problems using the national cyberinfrastructure

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