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We present an approach to model hardware configurations of complex manufacturing systems such as ASML's lithography machines. These hardware configurations consist of actuator and sensor elements which are controlled by system software that consists of over 35 million lines of code. To minimize the cost of testing and system integration, software simulators of hardware configurations are used - acting as virtual hardware platforms on which the real control software can be executed and tested. An important aspect in such simulation and testing is material flow (specifically wafer flow) in the machine. To support the effective and efficient realization of simulators covering material flow, we defined a domain specific language (DSL) for modelling the hardware configurations to be simulated, and used a model-driven engineering approach to generate the software components implementing the simulators. The DSL can be used to specify not only nominal (i.e. good weather) behaviour but also simulation based fault injection scenarios. The overall approach reduces the cost of early hardware-software integration and enables simulating scenarios that cannot be executed on real machines because they are difficult or hazardous to carry out.

Since their introduction in 1994 the Martin's metrics became popular in assessing object-oriented software architectures. While one of the Martin metrics, normalised distance from the main sequence Dn, has been originally designed with assessing individual packages, it has also been applied to assess quality of entire software architectures. The approach itself, however, has never been studied. In this paper we take the first step to formalising the Dn-based architecture assessment of Java open source software. We present two aggregate measures: average normalised distance from the main sequence Dmacrn, and parameter of the fitted statistical model lambda. Applying these measures to a carefully selected collection of benchmarks we obtain a set of reference values that can be used to assess quality of a system architecture. Furthermore, we show that applying the same measures to different versions of the same system provides valuable insights in system architecture evolution.

The normalized distance from the main sequence, denoted D_n, is a popular object-oriented metric introduced by Martin in 1994. While the metric has been designed for assessment of individual packages it has also been applied in practice to quality assessment of entire software architectures. This gap between the industrial practice and theoretical understanding has been recently addressed for Java open-source systems. Based on study of a benchmarks collection the authors proposed a statistical model characterizing (a) the average value of D_n, and (b) distribution of D_n. Contribution of the current work is twofold. First, we show feasibility of application of the D_n-based assessment above to commercial Java applications. Second, we validate the approach by showing that the results obtained are consistent with those obtained by means of a series of independent studies, such as layering, presence of cyclic dependencies and Chidamber's and Kemerer's metrics.