58 research outputs found

    Stratified Certification for k-Induction

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    Our recently proposed certification framework for bit-level k-induction-based model checking has been shown to be quite effective in increasing the trust of verification results even though it partially involved quantifier reasoning. In this paper we show how to simplify the approach by assuming reset functions to be stratified. This way it can be lifted to word-level and in principle to other theories where quantifier reasoning is difficult. Our new method requires six simple SAT checks and one polynomial-time check, allowing certification to remain in co-NP while the previous approach required five SAT checks and one QBF check. Experimental results show a substantial performance gain for our new approach. Finally we present and evaluate our new tool CERTIFAIGER-WL which is able to certify k-induction-based word-level model checking.Peer reviewe

    Parasol: Efficient Parallel Synthesis of Large Model Spaces

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    Formal analysis is an invaluable tool for software engineers, yet state-of-the-art formal analysis techniques suffer from well-known limitations in terms of scalability. In particular, some software design domains—such as tradeoff analysis and security analysis—require systematic exploration of potentially huge model spaces, which further exacerbates the problem. Despite this present and urgent challenge, few techniques exist to support the systematic exploration of large model spaces. This paper introduces Parasol, an approach and accompanying tool suite, to improve the scalability of large-scale formal model space exploration. Parasol presents a novel parallel model space synthesis approach, backed with unsupervised learning to automatically derive domain knowledge, guiding a balanced partitioning of the model space. This allows Parasol to synthesize the models in each partition in parallel, significantly reducing synthesis time and making large-scale systematic model space exploration for real-world systems more tractable. Our empirical results corroborate that Parasol substantially reduces (by 460% on average) the time required for model space synthesis, compared to state-of-the-art model space synthesis techniques relying on both incremental and parallel constraint solving technologies as well as competing, non-learning-based partitioning methods

    Falsification of Cyber-Physical Systems with Robustness-Guided Black-Box Checking

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    For exhaustive formal verification, industrial-scale cyber-physical systems (CPSs) are often too large and complex, and lightweight alternatives (e.g., monitoring and testing) have attracted the attention of both industrial practitioners and academic researchers. Falsification is one popular testing method of CPSs utilizing stochastic optimization. In state-of-the-art falsification methods, the result of the previous falsification trials is discarded, and we always try to falsify without any prior knowledge. To concisely memorize such prior information on the CPS model and exploit it, we employ Black-box checking (BBC), which is a combination of automata learning and model checking. Moreover, we enhance BBC using the robust semantics of STL formulas, which is the essential gadget in falsification. Our experiment results suggest that our robustness-guided BBC outperforms a state-of-the-art falsification tool.Comment: Accepted to HSCC 202

    Strong Induction in Hardware Model Checking

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    Symbolic Model checking is a widely used technique for automated verification of both hardware and software systems. Unbounded SAT-based Symbolic Model Checking (SMC) algorithms are very popular in hardware verification. The principle of strong induction is one of the first techniques for SMC. While elegant and simple to apply, properties as such can rarely be proven using strong induction and when they can be strengthened, there is no effective strategy to guess the depth of induction. It has been mostly displaced by techniques that compute inductive strengthenings based on interpolation and property directed reachability (PDR). In this thesis, we prove that strong induction is more concise than induction. We then present kAvy, an SMC algorithm that effectively uses strong induction to guide interpolation and PDR-style incremental inductive invariant construction. Unlike pure strong induction, kAvy uses PDR-style generalization to compute and strengthen an inductive trace. Unlike pure PDR, kAvy uses relative strong induction to construct an inductive invariant. The depth of induction is adjusted dynamically by minimizing a proof of unsatisfiability. We have implemented kAvy within the Avy Model Checker and evaluated it on HWMCC instances. Our results show that kAvy is more effective than both Avy and PDR, and that using strong induction leads to faster running time and solving more instances. Further, on a class of benchmarks, called shift, kAvy is orders of magnitude faster than Avy, PDR and pure strong induction

    Reaching for the Star: Tale of a Monad in Coq

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    Monadic programming is an essential component in the toolbox of functional programmers. For the pure and total programmers, who sometimes navigate the waters of certified programming in type theory, it is the only means to concisely implement the imperative traits of certain algorithms. Monads open up a portal to the imperative world, all that from the comfort of the functional world. The trend towards certified programming within type theory begs the question of reasoning about such programs. Effectful programs being encoded as pure programs in the host type theory, we can readily manipulate these objects through their encoding. In this article, we pursue the idea, popularized by Maillard [Kenji Maillard, 2019], that every monad deserves a dedicated program logic and that, consequently, a proof over a monadic program ought to take place within a Floyd-Hoare logic built for the occasion. We illustrate this vision through a case study on the SimplExpr module of CompCert [Xavier Leroy, 2009], using a separation logic tailored to reason about the freshness of a monadic gensym

    Satisfiability Checking of Multi-Variable TPTL with Unilateral Intervals Is PSPACE-Complete

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