Accelerated test execution using GPUs

As product life-cycles become shorter and the scale and complexity of systems increase, accelerating the execution of large test suites gains importance. Existing research has primarily focussed on techniques that reduce the size of the test suite. By contrast, we propose a technique that accelerates test execution, allowing test suites to run in a fraction of the original time, by parallel execution with a Graphics Processing Unit (GPU). Program testing, which is in essence execution of the same program with multiple sets of test data, naturally exhibits the kind of data parallelism that can be exploited with GPUs. Our approach simultaneously executes the program with one test case per GPU thread. GPUs have severe limitations, and we discuss these in the context of our approach and define the scope of our applications. We observe speed-ups up to a factor of 27 compared to single-core execution on conventional CPUs with embedded systems benchmark programs

Concurrent Program Verification with Invariant-Guided Underapproximation

Automatic verification of concurrent programs written in low-level languages like ANSI-C is an important task as multi-core architectures are gaining widespread adoption. Formal verification, although very valuable for this domain, rapidly runs into the state-explosion problem due to multiple thread interleavings. Recently, Bounded Model Checking (BMC) has been used for this purpose, which does not scale in practice. In this work, we develop a method to further constrain the search space for BMC techniques using underapproximations of data flow of shared memory and lazy demand-driven refinement of the approximation. A novel contribution of our method is that our underapproximation is guided by likely data-flow invariants mined from dynamic analysis and our refinement is based on proof-based learning. We have implemented our method in a prototype tool. Initial experiments on benchmark examples show potential performance benefit

Incremental bounded model checking for embedded software

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

Proving Safety with Trace Automata and Bounded Model Checking

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

Assisted coverage closure

Malfunction of safety-critical systems may cause damage to people and the environment. Software within those systems is rigorously designed and verified according to domain specific guidance, such as ISO26262 for automotive safety. This paper describes academic and industrial co-operation in tool development to support one of the most stringent of the requirements --- achieving full code coverage in requirements-driven testing. We present a verification workflow supported by a tool that integrates the coverage measurement tool RapiCover with the test-vector generator FShell. The tool assists closing the coverage gap by providing the engineer with test vectors that help in debugging coverage-related code quality issues and creating new test cases, as well as justifying the presence of unreachable parts of the code in order to finally achieve full effective coverage according to the required criteria. We illustrate the tool's practical utility on automotive industry benchmarks. It generates 8 times more MC/DC coverage than random search

Logico-numerical max-strategy iteration

Strategy iteration methods are used for solving fixed point equations. It has been shown that they improve precision in static analysis based on abstract interpretation and template abstract domains, e.g. intervals, octagons or template polyhedra. However, they are limited to numerical programs. In this paper, we propose a method for applying max-strategy iteration to logico-numerical programs, i.e. programs with numerical and Boolean variables, without explicitly enumerating the Boolean state space. The method is optimal in the sense that it computes the least fixed point w.r.t. the abstract domain; in particular, it does not resort to widening. Moreover, we give experimental evidence about the efficiency and precision of the approach

Generating test case chains for reactive systems

Testing of reactive systems is challenging because long input sequences are often needed to drive them into a state to test a desired feature. This is particularly problematic in on-target testing, where a system is tested in its real-life application environment and the amount of time required for resetting is high. This article presents an approach to discovering a test case chain—a single software execution that covers a group of test goals and minimizes overall test execution time. Our technique targets the scenario in which test goals for the requirements are given as safety properties. We give conditions for the existence and minimality of a single test case chain and minimize the number of test case chains if a single test case chain is infeasible. We report experimental results with our ChainCover tool for C code generated from Simulink models and compare it to state-of-the-art test suite generators

Lifting CDCL to template-based abstract domains for program verification

The success of Conflict Driven Clause Learning (CDCL) for Boolean satisfiability has inspired adoption in other domains. We present a novel lifting of CDCL to program analysis called Abstract Conflict Driven Learning for Programs (ACDLP). ACDLP alternates between model search, which performs over-approximate deduction with constraint propagation, and conflict analysis, which performs under-approximate abduction with heuristic choice. We instantiate the model search and conflict analysis algorithms with an abstract domain of template polyhedra, strictly generalizing CDCL from the Boolean lattice to a richer lattice structure. Our template polyhedra can express intervals, octagons and restricted polyhedral constraints over program variables. We have implemented ACDLP for automatic bounded safety verification of C programs. We evaluate the performance of our analyser by comparing with CBMC, which uses Boolean CDCL, and Astrée, a commercial abstract interpretation tool. We observe two orders of magnitude reduction in the number of decisions, propagations, and conflicts as well as a 1.5x speedup in runtime compared to CBMC. Compared to Astrée, ACDLP solves twice as many benchmarks and has much higher precision. This is the first instantiation of CDCL with a template polyhedra abstract domain

Tolerance to nitroglycerin through proteasomal down-regulation of aldehyde dehydrogenase-2 in a genetic mouse model of ascorbate deficiency: Nitrate tolerance in ascorbate deficiency

L-gulonolactone oxidase-deficient (Gulo(-/-)) mice were used to study the effects of ascorbate deficiency on aortic relaxation by nitroglycerin (GTN) with focus on changes in the expression and activity of vascular aldehyde dehydrogenase-2 (ALDH2), which catalyses GTN bioactivation