479 research outputs found

    Proving Safety with Trace Automata and Bounded Model Checking

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    Loop under-approximation is a technique that enriches C programs with additional branches that represent the effect of a (limited) range of loop iterations. While this technique can speed up the detection of bugs significantly, it introduces redundant execution traces which may complicate the verification of the program. This holds particularly true for verification tools based on Bounded Model Checking, which incorporate simplistic heuristics to determine whether all feasible iterations of a loop have been considered. We present a technique that uses \emph{trace automata} to eliminate redundant executions after performing loop acceleration. The method reduces the diameter of the program under analysis, which is in certain cases sufficient to allow a safety proof using Bounded Model Checking. Our transformation is precise---it does not introduce false positives, nor does it mask any errors. We have implemented the analysis as a source-to-source transformation, and present experimental results showing the applicability of the technique

    Incremental bounded model checking for embedded software

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    Program analysis is on the brink of mainstream usage in embedded systems development. Formal verification of behavioural requirements, finding runtime errors and test case generation are some of the most common applications of automated verification tools based on bounded model checking (BMC). Existing industrial tools for embedded software use an off-the-shelf bounded model checker and apply it iteratively to verify the program with an increasing number of unwindings. This approach unnecessarily wastes time repeating work that has already been done and fails to exploit the power of incremental SAT solving. This article reports on the extension of the software model checker CBMC to support incremental BMC and its successful integration with the industrial embedded software verification tool BTC EMBEDDED TESTER. We present an extensive evaluation over large industrial embedded programs, mainly from the automotive industry. We show that incremental BMC cuts runtimes by one order of magnitude in comparison to the standard non-incremental approach, enabling the application of formal verification to large and complex embedded software. We furthermore report promising results on analysing programs with arbitrary loop structure using incremental BMC, demonstrating its applicability and potential to verify general software beyond the embedded domain

    Verification and falsification of programs with loops using predicate abstraction

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    Predicate abstraction is a major abstraction technique for the verification of software. Data is abstracted by means of Boolean variables, which keep track of predicates over the data. In many cases, predicate abstraction suffers from the need for at least one predicate for each iteration of a loop construct in the program. We propose to extract looping counterexamples from the abstract model, and to parametrise the simulation instance in the number of loop iterations. We present a novel technique that speeds up the detection of long counterexamples as well as the verification of programs with loop

    Scaling Bounded Model Checking By Transforming Programs With Arrays

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    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

    Loop summarization using state and transition invariants

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    This paper presents algorithms for program abstraction based on the principle of loop summarization, which, unlike traditional program approximation approaches (e.g., abstract interpretation), does not employ iterative fixpoint computation, but instead computes symbolic abstract transformers with respect to a set of abstract domains. This allows for an effective exploitation of problem-specific abstract domains for summarization and, as a consequence, the precision of an abstract model may be tailored to specific verification needs. Furthermore, we extend the concept of loop summarization to incorporate relational abstract domains to enable the discovery of transition invariants, which are subsequently used to prove termination of programs. Well-foundedness of the discovered transition invariants is ensured either by a separate decision procedure call or by using abstract domains that are well-founded by construction. We experimentally evaluate several abstract domains related to memory operations to detect buffer overflow problems. Also, our light-weight termination analysis is demonstrated to be effective on a wide range of benchmarks, including OS device driver

    Danger Invariants

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    Fast and Precise Symbolic Analysis of Concurrency Bugs in Device Drivers

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    © 2015 IEEE.Concurrency errors, such as data races, make device drivers notoriously hard to develop and debug without automated tool support. We present Whoop, a new automated approach that statically analyzes drivers for data races. Whoop is empowered by symbolic pairwise lockset analysis, a novel analysis that can soundly detect all potential races in a driver. Our analysis avoids reasoning about thread interleavings and thus scales well. Exploiting the race-freedom guarantees provided by Whoop, we achieve a sound partial-order reduction that significantly accelerates Corral, an industrial-strength bug-finder for concurrent programs. Using the combination of Whoop and Corral, we analyzed 16 drivers from the Linux 4.0 kernel, achieving 1.5 - 20× speedups over standalone Corral
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