Many industrial real-time systems have evolved over a long period of time and were initially so simple that it was possible to predict consequences of adding new functionality by common sense. However, as the system evolves the possibility to predict the consequences of changes become more and more difficult unless models and analysis method can be used. In this paper we describe our approach to re-introducing analyzability into a complex real-time control system at ABB Robotics. The system consists of about 2 500 000 lines of code. Traditional real-time models and analyses, e.g. fixed priority analysis, were not applicable on this large and complex real-time system since the models are too simple for describing the systems behavior accurately, and the analyses are too pessimistic. The proposed method is based on analytical models and discrete-event based simulation of the system behavior based on these models. The models describe execution times as statistical distributions which are measured and calculated in the existing system. Simulation will not only enable models with statistical execution times, but also correctness criterion other than meeting deadlines, e.g. non-empty communication queues. Having accurate system models enable analysis of the impact on the temporal behavior of, e.g. customizing or maintaining the software. The case study presented in the paper shows the feasibility of the method. The method presented is applicable to a large class of complex real-time systems.
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