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February 2014

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2013-20: Time-Continuous Behaviour Comparison Based on Abstract Models
by Thomas Ströder 20 Feb '14

20 Feb '14

The following technical report is available from http://aib.informatik.rwth-aachen.de: Time-Continuous Behaviour Comparison Based on Abstract Models Jacob Palczynski AIB 2013-20 Model-based approaches in the development of embedded software become more and more widely spread. In these approaches, different models of one target system are the central elements. Re- cently, a growing number of algorithms and functionality of embedded software are designed using such model-based approaches, a prominent one is Rapid Control Prototyping. Its main advantage is the possibility to develop functionality of both controlling embedded software and controlled system in one modelling environment. Additionally, the developer can simulate the whole system and improve its performance, debug algorithms, etc. in early development stage. Important test phases are Software in the Loop and Hardware in the Loop. In this work, we discuss two problems that may occur during these phases. The first question is: Taking into account the continuous characteristics of system variables, how can we safeguard a consistent evolution of the development artefacts? To deal with this problem, test results are compared in order to safeguard a consistent evolution of the development artefacts. Since different artefacts were tested at different stages of the development under different conditions, we cannot expect that the results of each test case to be exactly equal, but at most similar. This similarity check of continuous systems is addressed in this work. The methodology presented here is based on behavioural black-box models of our system in the time domain on different levels of abstraction. A behaviour is represented by the input and output signals of a system and their interrelationship. On each level of abstraction, a system’s model is put up by a set of behaviours, each of which consists of input signals and the according output signals created by the system. The description of the signals themselves varies strongly, depending on the level of abstraction. For the comparison of two systems or artefacts, we have to introduce a similarity relation with respect to an abstract model. Two systems are similar with respect to an abstract model A, when their behaviours conform to this abstract model A. The central question is how we can find properties in measured signal data. To find a characteristic property in a set of measured signal data, we compute the cross correlation of the interpolated measured data and a template signal. By this, we find on which time interval of the measured data one property potentially occurs. Repeating this for all properties yields all occurrences of the properties in the system’s abstract behaviour model. In the end, we know that these systems conform the abstract model and therefore are similar with respect to the abstract model. The second problem we address is: How can we analyse correctly test results obtained from real time test devices that do not have certified clocks? The motivation here are possible differences in sample timing due to not exactly working hardware timers. Due to this, a drift occurs in the sampled data which leads to distorted signals and seemingly wrong timings of events. We compare such a signal to one obtained with a certified device by searching for certain properties, and obtaining the points in time of the occurrences. Using these time information, we can estimate the difference of the sample times, i.e. the drift. For the search for properties based we use the cross correlation approach already used for regression tests.

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2013-14: Automatic Abstraction for Bit-Vectors using Decision Procedures
by Thomas Ströder 03 Feb '14

03 Feb '14

The following technical report is available from http://aib.informatik.rwth-aachen.de: Automatic Abstraction for Bit-Vectors using Decision Procedures Jörg Brauer AIB 2013-14 This dissertation is concerned with abstract interpretation of programs whose semantics is defined over finite machine words. Most notably, the considered class of programs contains executable binary code, the analysis of which turns out demanding due to the complexity and the sheer number of involved operations. Challenging for correct yet precise abstract interpretation of binary code are transfer functions, which simulate the execution of any concrete operation in a program in an abstract domain. Crucially for correctness, over- and underflows need to be supported faithfully. This dissertation argues that transfer functions and abstractions for sequences of operations over finite machine words can precisely and efficiently be generated, which contrasts with classical methods that depend on handcrafted transfer functions. To support this statement, we present an approach that eliminates the time-consuming process of manually deriving transfer functions altogether. The core of our methods are specifications of the concrete semantics of sequences of operations, which are given in propositional Boolean logic. By utilizing SAT and SMT solvers, which can determine satisfiability of Boolean formulae, we show how to automatically synthesize optimal abstractions from such semantic specifications. The practicality of our method is highlighted using abstractions generated for a variety of numerical domains that are frequently used in abstract interpretation. The abstract domains considered in this dissertation are, most notably, intervals, value sets, octagons, convex polyhedra, arithmetical congruences, affine equalities, and polynomials of bounded degree. Importantly, all presented techniques automatically handle finiteness of machine words, which manifests itself in over- and underflows. Once the analysis of a program has terminated, an abstract interpreter often emits a warning that highlights a potential error in the analyzed program. Since abstract interpretation computes an over-approximation of the states reachable in a concrete execution, such a warning may be spurious. For this setting, we present variations of our methods, which compute complete abstractions. Then, it is possible to provide guarantees about actually reachable states, which allows us to do both, identify a warning as spurious or generate a legitimate counterexample trace.

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