Multi-variability modeling and realization for software derivation in industrial automation management
Miao Fang, G. Leyh, Jörg Dörr
Oct 2, 2016
Citations
4
Citations
Journal
Proceedings of the ACM/IEEE 19th International Conference on Model Driven Engineering Languages and Systems
Full text
Semantic Scholar
Key Takeaway Key Takeaway: The paper presents an approach that integrates feature modeling with domain-specific modeling languages (DSMLs) and code generation techniques to improve industrial automation management software development and derivation.
Abstract
The systems of industrial automation management (IAM) are in the domain of information systems. IAM systems have software components that support manufacturing processes. The operational parts of IAM 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 IAM 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 DSMLs and the improvement on automating software derivation. Finally, we present the lessons learned during the execution of this case study.