Model-driven engineering (MDE) is a software-engineering paradigm that is being introduced into a growing number of domains. One of the most important success factors for a new MDE approach is the availability of the appropriate tool support for it. Although the literature discusses the development of support tools, only a few reports and analyses are available about the development of tool support for real-life modeling languages and MDE approaches. The goal of this paper is to fill this gap through an experience report about developing a tool-suite prototype for an MDE approach for the process control domain that is capable of supporting the development of real-life process control software. Before the work presented in this paper an initial prototype tool suite was already developed. However, it was not able to adequately support industry-scale projects. The paper starts with an analysis of the past development of this already-existing laboratory prototype and then moves on to a report about the development of the industrial prototype, which is influenced by the findings of the analysis. Then a comparison between the two prototypes is made and the lessons learned are described, which may be useful to practitioners who attempt to develop support tools for an MDE approach that are useful in practice. The most important lesson learned is that when developing tool support for complex modeling languages, the traditional development approach should not be easily rejected.
Software is an important part of industrial process control systems. However, the state-of-the-practice for developing industrial process control software still has several key challenges that need to be addressed (e.g., migration to platforms of different vendors, lack of automation). This paper introduces a model-driven engineering approach to the development of industrial process control software, which is based on the ProcGraph domain-specific modeling language. The paper discusses and offers solutions to several of the development challenges that have not been addressed by existing techniques in the process controls domain. The contributions of the paper are a model-driven engineering approach for the industrial process control domain and a supporting tool infrastructure. The approach is demonstrated by a case study focused on the development of a control system for a TiO 2 (titanium dioxide) pigment production subprocess.
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