Modular Product Programs

Eilers, Marco; Müller, Péter; Hitz, Samuel

doi:10.1007/978-3-319-89884-1_18

Cited by 26 publications

(43 citation statements)

References 30 publications

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“…For example, consider a composition that "synchronizes" the two copies in each control structure (e.g. [14]). Such a composition runs the two copies of the loop in parallel until one copy exits the loop, and then continues to run the other copy.…”

Section: B Demonstrating the Interplay Between Self Composition And Imentioning

confidence: 99%

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Property Directed Self Composition

Shemer

Gurfinkel

Shoham

et al. 2019

Computer Aided Verification

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We address the problem of verifying k-safety properties: properties that refer to k interacting executions of a program. A prominent way to verify ksafety properties is by self composition. In this approach, the problem of checking k-safety over the original program is reduced to checking an "ordinary" safety property over a program that executes k copies of the original program in some order. The way in which the copies are composed determines how complicated it is to verify the composed program. We view this composition as provided by a semantic self composition function that maps each state of the composed program to the copies that make a move. Since the "quality" of a self composition function is measured by the ability to verify the safety of the composed program, we formulate the problem of inferring a self composition function together with the inductive invariant needed to verify safety of the composed program, where both are restricted to a given language. We develop a property-directed inference algorithm that, given a set of predicates, infers composition-invariant pairs expressed by Boolean combinations of the given predicates, or determines that no such pair exists. We implemented our algorithm and demonstrate that it is able to find self compositions that are beyond reach of existing tools.

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Section: B Demonstrating the Interplay Between Self Composition And Imentioning

confidence: 99%

“…The self composed program obtained by this composition is displayed in Figure 7. Figure 3 based on [14].…”

Section: B Demonstrating the Interplay Between Self Composition And Imentioning

confidence: 99%

Property Directed Self Composition

Shemer

Gurfinkel

Shoham

et al. 2019

Computer Aided Verification

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“…As mentioned in Section 1, existing techniques reduce relational verification to safety checking either by explicitly constructing a product program [Barthe et al 2011[Barthe et al , 2016Eilers et al 2018] or introducing a proof system where certain proof obligations can be discharged by an off-the-shelf safety checker [Barthe et al 2012;Benton 2004;Sousa and Dillig 2016]. In this paper, we adopt the latter approach and think of relational verification as the problem of searching for a proof within a relational program logic.…”

Section: Background On Relational Verificationmentioning

confidence: 99%

“…As stated in Section 1, relational verification problems are typically solved by reducing them to standard safety in one of several ways. Some approaches construct a new program that is safe iff the original relational verification problem is valid [Barthe et al 2011[Barthe et al , 2016[Barthe et al , 2004Eilers et al 2018]. Other approaches [Barthe et al 2012;Benton 2004; .…”

Section: Related Workmentioning

confidence: 99%

“…Interestingly, a common theme among all relational verification techniques is that they reduce the problem of proving a relational property to that of standard safety. For instance, given two programs P 1 and P 2 that need to obey some relational property, a popular approach is to construct a so-called product program P such that the relational property is valid if P obeys some safety property [Barthe et al 2011[Barthe et al , 2016Eilers et al 2018;Sousa et al 2018;Zaks and Pnueli 2008]. In a similar vein, many relational program logics [Benton 2004;Chen et al 2017;Sousa and Dillig 2016;Yang 2007] reduce relational verification to the problem of discharging a set of standard Hoare triples.…”

Section: Introductionmentioning

confidence: 99%

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Relational verification using reinforcement learning

Chen

Wei

et al. 2019

Proc. ACM Program. Lang.

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Relational verification aims to prove properties that relate a pair of programs or two different runs of the same program. While relational properties (e.g., equivalence, non-interference) can be verified by reducing them to standard safety, there are typically many possible reduction strategies, only some of which result in successful automated verification. Motivated by this problem, we propose a new relational verification algorithm that learns useful reduction strategies using reinforcement learning. Specifically, we show how to formulate relational verification as a Markov decision process (MDP) and use reinforcement learning to synthesize an optimal policy for the underlying MDP. The learned policy is then used to guide the search for a successful verification strategy. We have implemented this approach in a tool called Coeus and evaluate it on two benchmark suites. Our evaluation shows that Coeus solves significantly more problems within a given time limit compared to multiple baselines, including two state-of-the-art relational verification tools. CCS Concepts: • Software and its engineering → Software verification; • Theory of computation → Reinforcement learning.

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Automated Hypersafety Verification

Farzan

Vandikas

2019

Computer Aided Verification

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We propose an automated verification technique for hypersafety properties, which express sets of valid interrelations between multiple finite runs of a program. The key observation is that constructing a proof for a small representative set of the runs of the product program (i.e. the product of the several copies of the program by itself), called a reduction, is sufficient to formally prove the hypersafety property about the program. We propose an algorithm based on a counterexampleguided refinement loop that simultaneously searches for a reduction and a proof of the correctness for the reduction. We demonstrate that our tool Weaver is very effective in verifying a diverse array of hypersafety properties for a diverse class of input programs.

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Modular Product Programs

Cited by 26 publications

References 30 publications

Property Directed Self Composition

Property Directed Self Composition

Relational verification using reinforcement learning

Automated Hypersafety Verification

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