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Computation Tree Measurement Language (CTML) is a newly developed formal language that offers simultaneous model verification and performance evaluation measures. While the theory behind CTML has been established, the language has yet to be tested on a practical example. In this work, we wish to demonstrate the utility of CTML when applied to a real-world application based in manufacturing. Mobile manipulators may enable more flexible, dynamic workflows within industry. Therefore, an artifact-based performance measurement test method for mobile manipulator robots developed at the National Institute of Standards and Technology was selected for evaluation. Contributions of this work include the modeling of robot tasks implemented for the performance measurement test using Petri nets, as well as the formulation and execution of sample queries using CTML. To compare the numerical results, query support, ease of implementation, and empirical runtime of CTML to other temporal logics in such applications, the queries were re-formulated and evaluated using the PRISM Model Checker. Finally, a discussion is included that considers future extensions of this work, relative to other existing research, that could potentially enable the integration of CTML with Systems Modeling Language (SysML) and Product Life-cycle Management (PLM) software solutions.

In this work, we present a formal language, CTML, to reason over probabilistic systems. CTML extends stochastic temporal logics in a way that it takes a real value as input and output a real value in the range of [ 0 , ∞ ) , as opposed to 0/1 values as input and output, and it can nest real values. This allows CTML to express a rich set of queries towards the unification of model checking and performance evaluation. In fact, CTML covers PCTL. It can express a nontrivial subset of PLTL formulas that cannot be expressed by PCTL. The significance of this result is that the overall complexity of CTML is linear, as opposed to exponential as it is with PLTL, in the size of the operators for a given formula, and polynomial in the size of a given model. Moreover, CTML can express real-valued performance queries such as: “if a system encounters a failure, what is the expected time to reach a recovery state?” that cannot be expressed by a probabilistic model checking logic, because they are “probabilistic” at most. Along with the specification language, we present a set of algorithms for the evaluation of the language and show proofs for their correctness. Additionally, we include an application example and show experimental results.

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