Interpolation Properties and SAT-based Model Checking

Craig interpolation is a widespread method in verification, with important applications such as Predicate Abstraction, CounterExample Guided Abstraction Refinement and Lazy Abstraction With Interpolants. Most state-of-the-art model checking techniques based on interpolation require collections of interpolants to satisfy particular properties, to which we refer as "collectives"; they do not hold in general for all interpolation systems and have to be established for each particular system and verification environment. Nevertheless, no systematic approach exists that correlates the individual interpolation systems and compares the necessary collectives. This paper proposes a uniform framework, which encompasses (and generalizes) the most common collectives exploited in verification. We use it for a systematic study of the collectives and of the constraints they pose on propositional interpolation systems used in SAT-based model checking

Interpolant-Based Transition Relation Approximation

In predicate abstraction, exact image computation is problematic, requiring in the worst case an exponential number of calls to a decision procedure. For this reason, software model checkers typically use a weak approximation of the image. This can result in a failure to prove a property, even given an adequate set of predicates. We present an interpolant-based method for strengthening the abstract transition relation in case of such failures. This approach guarantees convergence given an adequate set of predicates, without requiring an exact image computation. We show empirically that the method converges more rapidly than an earlier method based on counterexample analysis.Comment: Conference Version at CAV 2005. 17 Pages, 9 Figure

A Markov Chain Model Checker

Markov chains are widely used in the context of performance and reliability evaluation of systems of various nature. Model checking of such chains with respect to a given (branching) temporal logic formula has been proposed for both the discrete [17,6] and the continuous time setting [4,8]. In this paper, we describe a prototype model checker for discrete and continuous-time Markov chains, the Erlangen Twente Markov Chain Checker $(E \vdash MC^2$), where properties are expressed in appropriate extensions of CTL. We illustrate the general bene ts of this approach and discuss the structure of the tool. Furthermore we report on first successful applications of the tool to non-trivial examples, highlighting lessons learned during development and application of $(E \vdash MC^2$)

A tool for model-checking Markov chains

Markov chains are widely used in the context of the performance and reliability modeling of various systems. Model checking of such chains with respect to a given (branching) temporal logic formula has been proposed for both discrete [34, 10] and continuous time settings [7, 12]. In this paper, we describe a prototype model checker for discrete and continuous-time Markov chains, the Erlangen-Twente Markov Chain Checker EÎMC2, where properties are expressed in appropriate extensions of CTL. We illustrate the general benefits of this approach and discuss the structure of the tool. Furthermore, we report on successful applications of the tool to some examples, highlighting lessons learned during the development and application of EÎMC2

Strengthening Model Checking Techniques with Inductive Invariants

This paper describes optimized techniques to efficiently compute and reap benefits from inductive invariants within SAT-based model checking. We address sequential circuit verification, and we consider both equivalences and implications between pairs of nodes in the logic networks. First, we present a very efficient dynamic procedure, based on equivalence classes and incremental SAT, specifically oriented to reduce the set of checked invariants. Then, we show how to effectively integrate the computation of inductive invariants within state-of-the-art SAT-based model checking procedures. Experiments (on more than 600 designs) show the robustness of our approach on verification instances on which stand-alone techniques fai

Automated incremental software verification

Software continuously evolves to meet rapidly changing human needs. Each evolved transformation of a program is expected to preserve important correctness and security properties. Aiming to assure program correctness after a change, formal verification techniques, such as Software Model Checking, have recently benefited from fully automated solutions based on symbolic reasoning and abstraction. However, the majority of the state-of-the-art model checkers are designed that each new software version has to be verified from scratch. In this dissertation, we investigate the new Formal Incremental Verification (FIV) techniques that aim at making software analysis more efficient by reusing invested efforts between verification runs. In order to show that FIV can be built on the top of different verification techniques, we focus on three complementary approaches to automated formal verification. First, we contribute the FIV technique for SAT-based Bounded Model Checking developed to verify programs with (possibly recursive) functions with respect to the set of pre-defined assertions. We present the function-summarization framework based on Craig interpolation that allows extracting and reusing over- approximations of the function behaviors. We introduce the algorithm to revalidate the summaries of one program locally in order to prevent re-verification of another program from scratch. Second, we contribute the technique for simulation relation synthesis for loop-free programs that do not necessarily contain assertions. We introduce an SMT-based abstraction- refinement algorithm that proceeds by guessing a relation and checking whether it is a simulation relation. We present a novel algorithm for discovering simulations symbolically, by means of solving ∀∃-formulas and extracting witnessing Skolem relations. Third, we contribute the FIV technique for SMT-based Unbounded Model Checking developed to verify programs with (possibly nested) loops. We present an algorithm that automatically derives simulations between programs with different loop structures. The automatically synthesized simulation relation is then used to migrate the safe inductive invariants across the evolution boundaries. Finally, we contribute the implementation and evaluation of all our algorithmic contributions, and confirm that the state-of-the-art model checking tools can successfully be extended by the FIV capabilities

Automatic Abstraction in SMT-Based Unbounded Software Model Checking

Software model checkers based on under-approximations and SMT solvers are very successful at verifying safety (i.e. reachability) properties. They combine two key ideas -- (a) "concreteness": a counterexample in an under-approximation is a counterexample in the original program as well, and (b) "generalization": a proof of safety of an under-approximation, produced by an SMT solver, are generalizable to proofs of safety of the original program. In this paper, we present a combination of "automatic abstraction" with the under-approximation-driven framework. We explore two iterative approaches for obtaining and refining abstractions -- "proof based" and "counterexample based" -- and show how they can be combined into a unified algorithm. To the best of our knowledge, this is the first application of Proof-Based Abstraction, primarily used to verify hardware, to Software Verification. We have implemented a prototype of the framework using Z3, and evaluate it on many benchmarks from the Software Verification Competition. We show experimentally that our combination is quite effective on hard instances.Comment: Extended version of a paper in the proceedings of CAV 201