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Abstract. Uppaal Stratego is a novel tool which facilitates generation, optimization, comparison as well as consequence and performance exploration of strategies for stochastic priced timed games in a user-friendly manner. The tool allows for efficient and flexible "strategy-space" exploration before adaptation in a final implementation by maintaining strategies as first class objects in the model-checking query language. The paper describes the strategies and their properties, construction and transformation algorithms and a typical tool usage scenario.

This paper offers a natural stochastic semantics of Networks of Priced Timed Automata (NPTA) based on races between components. The semantics provides the basis for satisfaction of Probabilistic Weighted CTL properties (PWCTL), conservatively extending the classical satisfaction of timed automata with respect to TCTL. In particular the extension allows for hard real-time properties of timed automata expressible in TCTL to be refined by performance properties, e.g. in terms of probabilistic guarantees of time-and cost-bounded properties. A second contribution of the paper is the application of Statistical Model Checking (SMC) to efficiently estimate the correctness of non-nested PWCTL model checking problems with a desired level of confidence, based on a number of independent runs of the NPTA. In addition to applying classical SMC algorithms, we also offer an extension that allows to efficiently compare performance properties of NPTAs in a parametric setting. The third contribution is an efficient tool implementation of our result and applications to several case studies.

Abstract. This chapter presents principles and techniques for modelbased black-box conformance testing of real-time systems using the Uppaal model-checking tool-suite. The basis for testing is given as a network of concurrent timed automata specified by the test engineer. Relativized input/output conformance serves as the notion of implementation correctness, essentially timed trace inclusion taking environment assumptions into account. Test cases can be generated offline and later executed, or they can be generated and executed online. For both approaches this chapter discusses how to specify test objectives, derive test sequences, apply these to the system under test, and assign a verdict.

Abstract. We propose the first tool for solving complex (some undecidable) problems of timed systems by using Statistical Model Checking (SMC). The tool monitors several runs of the system, and then relies on statistical algorithms to get an estimate of the correctness of the entire design. Contrary to other existing toolsets, ours relies on i) a natural stochastic semantics for networks of timed systems, ii) an engine capable to solve problems that are beyond the scope of classical model checkers, and iii) a friendly user interface. ContextTimed model checking (TMC) is a technique used to prove the absence of bugs in systems whose behaviors depend on real or discrete time constraints. The approach has been implemented in several tools [4,2,5] capable of handling case studies of industrial size. Unfortunately, many applications are still out of scope of TMC. This is due to the complexity of the timed behaviors, which can even make the problem undecidable.In a recent work [8], we presented Constant Slope Timed Automata (CSTA), that are timed systems in that clocks may have different rates (even potentially negative) in different locations. Such automata are as expressive as linear hybrid automata or priced timed automata, but the addition of features such as input and output modalities allows us to specify complex problems in an elegant manner. Unfortunately most of such problems are either undecidable or too complex to be solved with classical model checking approaches. In [8], we proposed to estimate undecidable problems by using Statistical Model Checking (SMC) [13,9]. SMC consists of monitoring some runs of the system and then uses a statistical algorithm to obtain an estimate for the system. Such simulation-based techniques were applied in other contexts where they outperformed classical model checking techniques with an order of magnitude [13,14,1].