Optimized model checking of multiple properties

Cabodi, Gianpiero; Nocco, Sergio

doi:10.1109/date.2011.5763279

Cited by 15 publications

(20 citation statements)

References 11 publications

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“…Isolation reduces the SAT formula by dismissing attackers that are outside the COI of the requirement to be verified. Isolation works similarly to COI reduction (see [13,15,7,18,14]), and it transforms Equation 4 into…”

Section: Isolation and Monotonicitymentioning

confidence: 99%

“…The works by Cabodi, Camurati and Quer [15], Cabodi et. al [13], and Cabodi and Nocco [14] present several useful techniques that can be used to improve the performance of model checking when verifying multiple properties, including COI reduction and property clustering. We also mention the work by Goldberg et al [20] where they consider the problem of efficiently checking a set of safety properties P 1 to P k by individually checking each property while assuming that all other properties are valid.…”

Section: Related Workmentioning

confidence: 99%

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Systematic Classification of Attackers via Bounded Model Checking

Rothstein-Morris

Sun

Chattopadhyay

2020

Lecture Notes in Computer Science

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In this work, we study the problem of verification of systems in the presence of attackers using bounded model checking. Given a system and a set of security requirements, we present a methodology to generate and classify attackers, mapping them to the set of requirements that they can break. A naive approach suffers from the same shortcomings of any large model checking problem, i.e., memory shortage and exponential time. To cope with these shortcomings, we describe two sound heuristics based on cone-of-influence reduction and on learning, which we demonstrate empirically by applying our methodology to a set of hardware benchmark systems.

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Section: Isolation and Monotonicitymentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Systematic Classification of Attackers via Bounded Model Checking

Rothstein-Morris

Sun

Chattopadhyay

2020

Lecture Notes in Computer Science

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“…The work most similar to ours is a property-clustering procedure based on COI similarity [7,8]. While a similar goal, their solution requires a quadratic number of comparisons between properties, rendering it prohibitively expensive on large testbenches.…”

Section: A Related Workmentioning

confidence: 99%

“…Despite the prevalence of multi-property testbenches, little research has addressed the problem of optimal grouping or clustering of properties into high-affinity groups. Selective past work [7,8] has experimentally demonstrated that ideal grouping may save substantial verification resource. However, no scalable online property grouping procedure has been provided; this potential was illustrated as a proof-of-concept using computationally-prohibitive offline grouping algorithms with undisclosed runtime.…”

Section: Introductionmentioning

confidence: 99%

Boosting Verification Scalability via Structural Grouping and Semantic Partitioning of Properties

Dureja

Baumgartner

Ivrii

et al. 2019

2019 Formal Methods in Computer Aided Design (FMCAD)

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From equivalence checking to functional verification to design-space exploration, industrial verification tasks entail checking a large number of properties on the same design. State-of-the-art tools typically solve all properties concurrently, or one-at-a-time. They do not optimally exploit subproblem sharing between properties, leaving an opportunity to save considerable verification resource via concurrent verification of properties with nearly identical cone of influence (COI). These high-affinity properties can be concurrently solved; the verification effort expended for one can be directly reused to accelerate the verification of the others, without hurting per-property verification resources through bloating COI size. We present a near-linear runtime algorithm for partitioning properties into provably high-affinity groups for concurrent solution. We also present an effective method to partition high-structural-affinity groups using semantic feedback, to yield an optimal multi-property localization abstraction solution. Experiments demonstrate substantial end-to-end verification speedups through these techniques, leveraging parallel solution of individual groups.

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“…. , P k into smaller clusters of properties [8], which is beyond the scope of this paper. To make joint verification more competitive, in Subsections 9-B and 9-C, we picked sixteen designs (eight designs per subsection) that have less than a thousand properties.…”

Section: Ja-verification Versus Joint Verificationmentioning

confidence: 99%

Efficient verification of multi-property designs (The benefit of wrong assumptions)

Goldberg¹,

Güdemann²,

Kroening³

et al. 2018

2018 Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE)

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We consider the problem of efficiently checking a set of safety properties P 1 , . . . , P k of one design. We introduce a new approach called JA-verification, where JA stands for "Just-Assume" (as opposed to "assume-guarantee"). In this approach, when proving a property P i , one assumes that every property P j for j = i holds. The process of proving properties either results in showing that P 1 , . . . , P k hold without any assumptions or finding a "debugging set" of properties. The latter identifies a subset of failed properties that are the first to break. The design behaviors that cause the properties in the debugging set to fail must be fixed first. Importantly, in our approach, there is no need to prove the assumptions used. We describe the theory behind our approach and report experimental results that demonstrate substantial gains in performance, especially in the cases where a small debugging set exists. * This is an extended version of the paper published at DATE-2018.

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Optimized model checking of multiple properties

Cited by 15 publications

References 11 publications

Systematic Classification of Attackers via Bounded Model Checking

Systematic Classification of Attackers via Bounded Model Checking

Boosting Verification Scalability via Structural Grouping and Semantic Partitioning of Properties

Efficient verification of multi-property designs (The benefit of wrong assumptions)

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