Scaling Bounded Model Checking By Transforming Programs With Arrays

Bounded Model Checking is one the most successful techniques for finding bugs in program. However, model checkers are resource hungry and are often unable to verify programs with loops iterating over large arrays.We present a transformation that enables bounded model checkers to verify a certain class of array properties. Our technique transforms an array-manipulating (ANSI-C) program to an array-free and loop-free (ANSI-C) program thereby reducing the resource requirements of a model checker significantly. Model checking of the transformed program using an off-the-shelf bounded model checker simulates the loop iterations efficiently. Thus, our transformed program is a sound abstraction of the original program and is also precise in a large number of cases - we formally characterize the class of programs for which it is guaranteed to be precise. We demonstrate the applicability and usefulness of our technique on both industry code as well as academic benchmarks

Automatic Verification of Embedded System Code Manipulating Dynamic Structures Stored in Contiguous Regions

International audienceUser-space programs rely on memory allocation primitives when they need to construct dynamic structures such as lists or trees. However, low-level OS kernel services and embedded device drivers typically avoid resorting to an external memory allocator in such cases, and store structure elements in contiguous arrays instead. This programming pattern leads to very complex code, based on data-structures that can be viewed and accessed either as arrays or as chained dynamic structures. The code correctness then depends on intricate invariants mixing both aspects. We propose a static analysis that is able to verify such programs. It relies on the combination of abstractions of the allocator array and of the dynamic structures built inside it. This approach allows to integrate program reasoning steps inherent in the array and in the chained structure into a single abstract interpretation. We report on the successful verification of several embedded OS kernel services and drivers

An array content static analysis based on non-contiguous partitions

International audienceConventional array partitioning analyses split arrays into contiguous partitions to infer properties of sets of cells. Such analyses cannot group together non contiguous cells, even when they have similar properties. In this paper, we propose an abstract domain which utilizes semantic properties to split array cells into groups. Cells with similar properties will be packed into groups and abstracted together. Additionally, groups are not necessarily contiguous. This abstract domain allows to infer complex array invariants in a fully automatic way. Experiments on examples from the Minix 1.1 memory management and a tiny industrial operating system demonstrate the effectiveness of the analysis

Abstraction of Arrays Based on Non Contiguous Partitions

International audienceArray partitioning analyses split arrays into contiguous parti-tions to infer properties of cell sets. Such analyses cannot group together non contiguous cells, even when they have similar properties. In this paper, we propose an abstract domain which utilizes semantic properties to split array cells into groups. Cells with similar properties will be packed into groups and abstracted together. Additionally, groups are not necessarily contiguous. This abstract domain allows to infer complex array invariants in a fully automatic way. Experiments on examples from the Minix 1.1 memory management demonstrate its effectiveness

Abstraction of Arrays Based on Non Contiguous Partitions

International audienceArray partitioning analyses split arrays into contiguous parti-tions to infer properties of cell sets. Such analyses cannot group together non contiguous cells, even when they have similar properties. In this paper, we propose an abstract domain which utilizes semantic properties to split array cells into groups. Cells with similar properties will be packed into groups and abstracted together. Additionally, groups are not necessarily contiguous. This abstract domain allows to infer complex array invariants in a fully automatic way. Experiments on examples from the Minix 1.1 memory management demonstrate its effectiveness

Computer Aided Verification

This open access two-volume set LNCS 11561 and 11562 constitutes the refereed proceedings of the 31st International Conference on Computer Aided Verification, CAV 2019, held in New York City, USA, in July 2019. The 52 full papers presented together with 13 tool papers and 2 case studies, were carefully reviewed and selected from 258 submissions. The papers were organized in the following topical sections: Part I: automata and timed systems; security and hyperproperties; synthesis; model checking; cyber-physical systems and machine learning; probabilistic systems, runtime techniques; dynamical, hybrid, and reactive systems; Part II: logics, decision procedures; and solvers; numerical programs; verification; distributed systems and networks; verification and invariants; and concurrency

Computer Aided Verification

This open access two-volume set LNCS 11561 and 11562 constitutes the refereed proceedings of the 31st International Conference on Computer Aided Verification, CAV 2019, held in New York City, USA, in July 2019. The 52 full papers presented together with 13 tool papers and 2 case studies, were carefully reviewed and selected from 258 submissions. The papers were organized in the following topical sections: Part I: automata and timed systems; security and hyperproperties; synthesis; model checking; cyber-physical systems and machine learning; probabilistic systems, runtime techniques; dynamical, hybrid, and reactive systems; Part II: logics, decision procedures; and solvers; numerical programs; verification; distributed systems and networks; verification and invariants; and concurrency