The software model checker Blast

Beyer, Dirk; Henzinger, Thomas A.; Jhala, Ranjit; Majumdar, Rupak

doi:10.1007/s10009-007-0044-z

Cited by 450 publications

(256 citation statements)

References 70 publications

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“…Model extraction was pioneered by FeaVer [6], which made it possible to extract PROMELA models from C code using user-provided abstractions, and Java PathFinder [7] which did the same for Java programs. The approach has successfully been refined using counter-example guided abstraction refinement (CEGAR) [4] which was first implemented by Microsoft SLAM [1], which extracts and automatically refines abstractions from C code for Microsoft Windows device drivers, and refined by BLAST [2]. While the tools for model-extraction support a full development cycle by abstraction refinement and reuse for modified implementations, the idea of combining the two approaches is to the best of our knowledge new.…”

Section: Discussionmentioning

confidence: 99%

“…If not, the abstraction used to extract the model is refined and the cycle restarts. This approach is rarely used in the high-level Petri net world, but is employed by, e.g., FeaVer [6] to translate C code to PROMELA code usable in SPIN [8], Java PathFinder [7] to translate Java programs to PROMELA, SLAM [1] for automatically translating C device drivers to boolean programs, BLAST [2] for model-checking C programs, and many other tools. Both of these approaches may terminate the loop without providing a definite response.…”

Section: Introductionmentioning

confidence: 99%

“…The restricted class of PP-CPNs allows us to automatically generate executable code from the model. Derivation of abstractions of the data-types used can be done by the user or automatically using counter-example guided abstraction refinement (CEGAR) [4] as implemented in SLAM [1] and BLAST [2]. CEGAR automatically improves abstractions by replaying errors found in an abstract model on the original program and using about why a given error-trace cannot be replayed in the original program to refine the abstraction.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Verifying Parallel Algorithms and Programs Using Coloured Petri Nets

Westergaard

2012

Transactions on Petri Nets and Other Models of Concurrency VI

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Abstract. Coloured Petri nets have proved to be a useful formalism for modeling distributed algorithms, i.e., algorithms where nodes communicate via message passing. Here we describe an approach for modeling parallel algorithms and programs, i.e., algorithms and programs where processes communicate via shared memory. The model is verified for correctness, here to prove absence of mutual exclusion violations and to find dead-and live-locks. The approach can be used in a model-driven development approach, where code is generated from a model, in a modelextraction approach, where a model is extracted from running code, or using a combination of the two, supporting extracting a model from an abstract description and generation of correct implementation code. We illustrate our idea by applying the technique to a parallel implementation of explicit state-space exploration.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Verifying Parallel Algorithms and Programs Using Coloured Petri Nets

Westergaard

2012

Transactions on Petri Nets and Other Models of Concurrency VI

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“…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%

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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“…Furthermore, we compared our tool against the results of the extensive study reported in [7] for the state-of-the-art OD tools CpaChecker [7], Blast [6], and SatAbs [12]. Both uUfo and cpUfo configurations are able to solve all buggy transmitter examples.…”

Section: Implementation and Evaluationmentioning

confidence: 99%

From Under-Approximations to Over-Approximations and Back

Albarghouthi

Gurfinkel

Chećhik

2012

Tools and Algorithms for the Construction and Analysis of Systems

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Abstract. Current approaches to software model checking can be divided into over-approximation-driven (OD) and under-approximationdriven (UD). OD approaches maintain an abstraction of the transition relation of a program and use abstract reachability to build an inductive invariant (or find a counterexample). At the other extreme, UD approaches attempt to construct inductive invariants by generalizing from finite paths through the control-flow graph of the program.In this paper, we present Ufo, an algorithm that unifies OD and UD approaches in order to leverage both of their advantages. Ufo is parameterized by the degree to which over-and under-approximations drive the analysis. At one extreme, Ufo is a novel interpolation-based (UD) algorithm that generates interpolants to label (refine) multiple program paths using a single SMT solver query. At the other extreme, Ufo uses an abstract domain to drive the analysis, while using interpolants to strengthen the abstraction.We have implemented Ufo in LLVM and applied it to programs from the Competition on Software Verification. Our experimental results demonstrate the utility of our algorithm and the benefits of combining UD and OD approaches.

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The software model checker Blast

Cited by 450 publications

References 70 publications

Verifying Parallel Algorithms and Programs Using Coloured Petri Nets

Verifying Parallel Algorithms and Programs Using Coloured Petri Nets

Verification as Learning Geometric Concepts

From Under-Approximations to Over-Approximations and Back

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