Improving Strategies via SMT Solving

We consider the problem of computing numerical invariants of programs by abstract interpretation. Our method eschews two traditional sources of imprecision: (i) the use of widening operators for enforcing convergence within a finite number of iterations (ii) the use of merge operations (often, convex hulls) at the merge points of the control flow graph. It instead computes the least inductive invariant expressible in the domain at a restricted set of program points, and analyzes the rest of the code en bloc. We emphasize that we compute this inductive invariant precisely. For that we extend the strategy improvement algorithm of [Gawlitza and Seidl, 2007]. If we applied their method directly, we would have to solve an exponentially sized system of abstract semantic equations, resulting in memory exhaustion. Instead, we keep the system implicit and discover strategy improvements using SAT modulo real linear arithmetic (SMT). For evaluating strategies we use linear programming. Our algorithm has low polynomial space complexity and performs for contrived examples in the worst case exponentially many strategy improvement steps; this is unsurprising, since we show that the associated abstract reachability problem is Pi-p-2-complete

Succinct Representations for Abstract Interpretation

Abstract interpretation techniques can be made more precise by distinguishing paths inside loops, at the expense of possibly exponential complexity. SMT-solving techniques and sparse representations of paths and sets of paths avoid this pitfall. We improve previously proposed techniques for guided static analysis and the generation of disjunctive invariants by combining them with techniques for succinct representations of paths and symbolic representations for transitions based on static single assignment. Because of the non-monotonicity of the results of abstract interpretation with widening operators, it is difficult to conclude that some abstraction is more precise than another based on theoretical local precision results. We thus conducted extensive comparisons between our new techniques and previous ones, on a variety of open-source packages.Comment: Static analysis symposium (SAS), Deauville : France (2012

Invariant Generation through Strategy Iteration in Succinctly Represented Control Flow Graphs

We consider the problem of computing numerical invariants of programs, for instance bounds on the values of numerical program variables. More specifically, we study the problem of performing static analysis by abstract interpretation using template linear constraint domains. Such invariants can be obtained by Kleene iterations that are, in order to guarantee termination, accelerated by widening operators. In many cases, however, applying this form of extrapolation leads to invariants that are weaker than the strongest inductive invariant that can be expressed within the abstract domain in use. Another well-known source of imprecision of traditional abstract interpretation techniques stems from their use of join operators at merge nodes in the control flow graph. The mentioned weaknesses may prevent these methods from proving safety properties. The technique we develop in this article addresses both of these issues: contrary to Kleene iterations accelerated by widening operators, it is guaranteed to yield the strongest inductive invariant that can be expressed within the template linear constraint domain in use. It also eschews join operators by distinguishing all paths of loop-free code segments. Formally speaking, our technique computes the least fixpoint within a given template linear constraint domain of a transition relation that is succinctly expressed as an existentially quantified linear real arithmetic formula. In contrast to previously published techniques that rely on quantifier elimination, our algorithm is proved to have optimal complexity: we prove that the decision problem associated with our fixpoint problem is in the second level of the polynomial-time hierarchy.Comment: 35 pages, conference version published at ESOP 2011, this version is a CoRR version of our submission to Logical Methods in Computer Scienc

Méthodes logico-numériques pour la vérification des systèmes discrets et hybrides

Cette thèse étudie la vérification automatique de propriétés de sûreté de systèmes logico-numériques discrets ou hybrides. Ce sont des systèmes ayant des variables booléennes et numériques et des comportements discrets et continus. Notre approche est fondée sur l'analyse statique par interprétation abstraite. Nous adressons les problèmes suivants : les méthodes d'interprétation abstraite numériques exigent l'énumération des états booléens, et par conséquent, ils souffrent du probléme d'explosion d'espace d'états. En outre, il y a une perte de précision due à l'utilisation d'un opérateur d'élargissement afin de garantir la terminaison de l'analyse. Par ailleurs, nous voulons rendre les méthodes d'interprétation abstraite accessibles à des langages de simulation hybrides. Dans cette thèse, nous généralisons d'abord l'accélération abstraite, une méthode qui améliore la précision des invariants numériques inférés. Ensuite, nous montrons comment étendre l'accélération abstraite et l'itération de max-stratégies à des programmes logico-numériques, ce qui aide à améliorer le compromis entre l'efficacité et la précision. En ce qui concerne les systèmes hybrides, nous traduisons le langage de programmation synchrone et hybride Zelus vers les automates hybrides logico-numériques, et nous étendons les méthodes d'analyse logico-numérique aux systèmes hybrides. Enfin, nous avons mis en oeuvre les méthodes proposées dans un outil nommé ReaVer et nous fournissons des résultats expérimentaux. En conclusion, cette thèse propose une approche unifiée à la vérification de systèmes logico-numériques discrets et hybrides fondée sur l'interprétation abstraite qui est capable d'intégrer des méthodes d'interprétation abstraite numériques sophistiquées tout en améliorant le compromis entre l'efficacité et la précision.This thesis studies the automatic verification of safety properties of logico-numerical discrete and hybrid systems. These systems have Boolean and numerical variables and exhibit discrete and continuous behavior. Our approach is based on static analysis using abstract interpretation. We address the following issues: Numerical abstract interpretation methods require the enumeration of the Boolean states, and hence, they suffer from the state space explosion problem. Moreover, there is a precision loss due to widening operators used to guarantee termination of the analysis. Furthermore, we want to make abstract interpretation-based analysis methods accessible to simulation languages for hybrid systems. In this thesis, we first generalize abstract acceleration, a method that improves the precision of the inferred numerical invariants. Then, we show how to extend abstract acceleration and max-strategy iteration to logico-numerical programs while improving the trade-off between efficiency and precision. Concerning hybrid systems, we translate the Zelus hybrid synchronous programming language to logico-numerical hybrid automata and extend logico-numerical analysis methods to hybrid systems. Finally, we implemented the proposed methods in ReaVer, a REActive System VERification tool, and provide experimental results. Concluding, this thesis proposes a unified approach to the verification of discrete and hybrid logico-numerical systems based on abstract interpretation, which is capable of integrating sophisticated numerical abstract interpretation methods while successfully trading precision for efficiency.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF