Простой алгоритм решения задачи покрытия для монотонных счетчиковых систем

An algorithm for solving the coverability problem for monotonic counter systems is presented. The solvability of this problem is well-known, but the algorithm is interesting due to its simplicity. The algorithm has emerged as a simplification of a certain procedure of a supercompiler application (a program specializer based on V.F. Turchin's supercompilation) to a program encoding a monotonic counter system along with initial and target sets of states and from the proof that under some conditions the procedure terminates and solves the coverability problem.Предложен алгоритм решения задачи покрытия для монотонных счетчиковых систем. Разрешимость этой задачи хорошо известна, но данный алгоритм интересен своей простотой. Он возник из упрощения некоторой итеративной процедуры применения суперкомпилятора (специализатора программ, основанного на методе суперкомпиляции В.Ф. Турчина) к программе, кодирующей счетчиковую систему и начальное и целевое множества состояний, и из доказательства, что при определенных условиях эта процедура завершается и решает задачу покрытия

Invariant Generation for Multi-Path Loops with Polynomial Assignments

Program analysis requires the generation of program properties expressing conditions to hold at intermediate program locations. When it comes to programs with loops, these properties are typically expressed as loop invariants. In this paper we study a class of multi-path program loops with numeric variables, in particular nested loops with conditionals, where assignments to program variables are polynomial expressions over program variables. We call this class of loops extended P-solvable and introduce an algorithm for generating all polynomial invariants of such loops. By an iterative procedure employing Gr\"obner basis computation, our approach computes the polynomial ideal of the polynomial invariants of each program path and combines these ideals sequentially until a fixed point is reached. This fixed point represents the polynomial ideal of all polynomial invariants of the given extended P-solvable loop. We prove termination of our method and show that the maximal number of iterations for reaching the fixed point depends linearly on the number of program variables and the number of inner loops. In particular, for a loop with m program variables and r conditional branches we prove an upper bound of m*r iterations. We implemented our approach in the Aligator software package. Furthermore, we evaluated it on 18 programs with polynomial arithmetic and compared it to existing methods in invariant generation. The results show the efficiency of our approach

Higher-Order Pattern Anti-Unification in Linear Time

We present a rule-based Huet’s style anti-unification algorithm for simply typed lambda-terms, which computes a least general higher-order pattern generalization. For a pair of arbitrary terms of the same type, such a generalization always exists and is unique modulo α-equivalence and variable renaming. With a minor modification, the algorithm works for untyped lambda-terms as well. The time complexity of both algorithms is linear.This research has been partially supported by the Austrian Science Fund (FWF) project SToUT (P 24087-N18), the Upper Austrian Government strategic program “Innovatives OÖ 2010plus”, the MINECO projects RASO (TIN2015-71799-C2-1-P) and HeLo (TIN2012-33042), the MINECO/FEDER UE project LoCoS (TIN2015-66293-R) and the UdG project MPCUdG2016/055.Peer Reviewe

Global Guidance for Local Generalization in Model Checking

SMT-based model checkers, especially IC3-style ones, are currently the most effective techniques for verification of infinite state systems. They infer global inductive invariants via local reasoning about a single step of the transition relation of a system, while employing SMT-based procedures, such as interpolation, to mitigate the limitations of local reasoning and allow for better generalization. Unfortunately, these mitigations intertwine model checking with heuristics of the underlying SMT-solver, negatively affecting stability of model checking. In this paper, we propose to tackle the limitations of locality in a systematic manner. We introduce explicit global guidance into the local reasoning performed by IC3-style algorithms. To this end, we extend the SMT-IC3 paradigm with three novel rules, designed to mitigate fundamental sources of failure that stem from locality. We instantiate these rules for the theory of Linear Integer Arithmetic and implement them on top of SPACER solver in Z3. Our empirical results show that GSPACER, SPACER extended with global guidance, is significantly more effective than both SPACER and sole global reasoning, and, furthermore, is insensitive to interpolation.Comment: Published in CAV 202

Parallel Solver of Large Systems of Linear Inequalities Using Fourier-Motzkin Elimination

Fourier-Motzkin elimination is a computationally expensive but powerful method to solve a system of linear inequalities. These systems arise e.g. in execution order analysis for loop nests or in integer linear programming. This paper focuses on the analysis, design and implementation of a parallel solver for distributed memory for large systems of linear inequalities using the Fourier-Motzkin elimination algorithm. We also measure the speedup of parallel solver and prove that this implementation results in good scalability