Multi-Variability Modeling and Realization for Software Derivation in Industrial Automation Management

The systems of industrial automation nianagement (TAM) are in the domain of information systems. IAM systems have software components that support manufacturing processes. The operational parts of TAM coordinate highly plug compatible hardware. devices. These functions lead to process and topology variability, which result in development and reuse challenges for software engineers in practice. This paper presents an approach aiming at improving the development and derivation of one TAM software family within Siemens. The approach integrates feature modeling with domain -specific modeling languages (DSMLs) for variability representation. Moreover, by combining code generation techniques, the configuration of variability models can be used to automate the software derivation. We report on a case study of applying the approach in practice. The outcome shows the enhancement of variability representation by introducing DSIN1Ls and the improvement on automating software derivation. Finally, we present the lessons learned during the execution of this case study.