Efficient detection of vacuity in ACTL formulas

Beer, Ilan; Ben-David, Shoham; Eisner, Cindy; Rodeh, Yoav

doi:10.1007/3-540-63166-6_28

Cited by 100 publications

(131 citation statements)

References 11 publications

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“…Beer et al [68] use witness formulas to detect vacuity for a subset of ACTL (CTL with only A quantified temporal operators). This method is extended to CTL* by Kupferman and Vardi [69].…”

Section: Vacuity Based Coveragementioning

confidence: 99%

Testing with model checkers: a survey

Fraser

Wotawa

Ammann

2008

Software Testing Verif & Rel

146

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Abstract.About a decade after the initial proposal to use model checkers for the generation of test cases we take a look at the achievements in this field of research. Model checkers are formal verification tools, capable of providing counterexamples to violated properties. Normally, these counterexamples are meant to guide an analyst when searching for the root cause of a property violation. They are, however, also very useful as test cases. Many different approaches have been presented, many problems have been solved, yet many issues remain. This survey paper reviews the state of the art in testing with model checkers.

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“…Beer et al [68] use witness formulas to detect vacuity for a subset of ACTL (CTL with only A quantified temporal operators). This method is extended to CTL* by Kupferman and Vardi [69].…”

Section: Vacuity Based Coveragementioning

confidence: 99%

Testing with model checkers: a survey

Fraser

Wotawa

Ammann

2008

Software Testing Verif & Rel

146

View full text Add to dashboard Cite

show abstract

“…The first attempt to formulate and automate vacuity detection is due to Beer et al [2]. They consider a property ϕ to be vacuous if ϕ contains a subformula ψ such that replacing ψ by any other formula does not affect the satisfaction of ϕ.…”

Section: Defining Vacuitymentioning

confidence: 99%

“…Vacuity detection [2,18,21,1] is an automatic sanity check that can be applied after a positive model-checking run in order to gain confidence that the model and the property capture the desired behaviours. Informally, a property is said to be vacuous if it has a subformula which is not relevant to its satisfaction, or if the property itself is a tautology.…”

Section: Introductionmentioning

confidence: 99%

Exploiting Resolution Proofs to Speed Up LTL Vacuity Detection for BMC

Simmonds

Davies

Gurfinkel

et al. 2007

Formal Methods in Computer Aided Design (FMCAD'07)

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Abstract.When model-checking reports that a property holds on a model, vacuity detection increases user confidence in this result by checking that the property is satisfied in the intended way. While vacuity detection is effective, it is a relatively expensive technique requiring many additional modelchecking runs. We address the problem of efficient vacuity detection for Bounded Model Checking (BMC) of LTL properties, presenting three partial vacuity detection methods based on the efficient analysis of the resolution proof produced by a successful BMC run. In particular, we define a characteristic of resolution proofs -peripherality -and prove that if a variable is a source of vacuity, then there exists a resolution proof in which this variable is peripheral. Our vacuity detection tool, VaqTree, uses these methods to detect vacuous variables, decreasing the total number of model-checking runs required to detect all sources of vacuity.

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“…The property P → Q has also been studied in the context of vacuity detection in [4,18,21], where P → Q is said to be vacuously satisfied whenever P is false. This is because Q is often the property that the user intends to check, while P is only a pre-condition.…”

Section: More Related Workmentioning

confidence: 99%

Whodunit? Causal Analysis for Counterexamples

Wang¹,

Yang

Ivančić³

et al. 2006

Automated Technology for Verification and Analysis

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Abstract. Although the counterexample returned by a model checker can help in reproducing the symptom related to a defect, a significant amount of effort is often required for the programmer to interpret it in order to locate the cause. In this paper, we provide an automated procedure to zoom in to potential software defects by analyzing a single concrete counterexample. Our analysis relies on extracting from the counterexample a syntactic-level proof of infeasibility, i.e., a minimal set of word-level predicates that contradict with each other. The procedure uses an efficient weakest pre-condition algorithm carried out on a single concrete execution path, which is significantly more scalable than other model checking based approaches. Unlike most of the existing methods, we do not need additional execution traces other than the buggy one. We use public-domain examples to demonstrate the effectiveness of our new algorithm.

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Efficient detection of vacuity in ACTL formulas

Cited by 100 publications

References 11 publications

Testing with model checkers: a survey

Testing with model checkers: a survey

Exploiting Resolution Proofs to Speed Up LTL Vacuity Detection for BMC

Whodunit? Causal Analysis for Counterexamples

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