We present algorithms and techniques for the repair of timed system models, given as networks of timed automata (NTA). The repair is based on an analysis of timed diagnostic traces (TDTs) that are computed by real-time model checking tools, such as UPPAAL, when they detect the violation of a timed safety property. We present an encoding of TDTs in linear real arithmetic and use the MaxSMT capabilities of the SMT solver Z3 to compute possible repairs to clock bound values that minimize the necessary changes to the automaton. We then present an admissibility criterion, called functional equivalence, that assesses whether a proposed repair is admissible in the overall context of the NTA. We have implemented a proof-of-concept tool called TARTAR for the repair and admissibility analysis. To illustrate the method, we have considered a number of case studies taken from the literature and automatically injected changes to clock bounds to generate faulty mutations. Our technique is able to compute a feasible repair for 91% of the faults detected by UPPAAL in the generated mutants.
We present TarTar , an automatic repair analysis tool that, given a timed diagnostic trace (TDT) obtained during the model checking of a timed automaton model, suggests possible syntactic repairs of the analyzed model. The suggested repairs include modified values for clock bounds in location invariants and transition guards, adding or removing clock resets, etc. The proposed repairs guarantee that the given TDT is no longer feasible in the repaired model, while preserving the overall functional behavior of the system. We give insights into the design and architecture of TarTar , and show that it can successfully repair 69% of the seeded errors in system models taken from a diverse suite of case studies.
Abstract. In this paper we present an automated translation from the systems engineering modeling language SysML into the input languages of the NuSMV, Prism and Spin model checkers. A special focus of this work is the semantics of the communication mechanisms used in a syntactic fragment of SysML, in particular synchronous and asynchronous, broadcast and buffered communication. In order to achieve generality of our approach, which supports establishing the consistency of the translation as well as enabling easy adaption between different source and target languages, we use a model based transformation approach. In particular, we use the ATLAS Transformation Language (ATL) framework that is nicely integrated in the Eclipse Modeling Framework (EMF) and in the Meta-Object Facility. We illustrate the application of this model transformation approach using an airbag system as a case study.
Causality Checking [LL13a] has been proposed as a finite state space exploration technique which computes ordered sequences of events that are considered to cause the violation of a reachability property. A crucial point in the implementation of Causality Checking is the computation and storage of all minimal counterexamples found during state space exploration. We refer to the set of all minimal counterexamples as a causal trace set. However, the Duplicate State Prefix Matching (DSPM) Algorithm that is currently used in Causality Checking only under-approximates the causal trace set. As we argue, without the approximation the DSPM algorithm is inefficient. We propose the, to the best of our knowledge, first efficient algorithm that precisely computes a causal trace set, avoiding approximation, called Causal Trace Backward Search (CTBS). We compare the DSPM and CTBS a lgorithms with respect to their worst case complexities, and by applying them to several case studies.
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