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D. Winkler, F. Ekaputra, S. Biffl:
"AutomationML Review Support in Multi-Disciplinary Engineering Environments";
Talk: 21st IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2016), Berlin, Germany; 2016-09-06 - 2016-09-09; in: "Proceedings of the 21st IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)", IEEE, (2016), ISBN: 978-1-5090-1314-2; 1 - 8.

In Multi-Disciplinary Engineering
(MDE) environments, the engineering of industrial production
systems requires the collaboration of engineers coming from
different disciplines. Engineers typically apply discipline specific
tools and data models with limited collaboration capabilities.
These loosely coupled tools and heterogeneous data models
hinder efficient change management and defect detection, which
makes MDE projects unnecessarily risky and error prone.
[Objective] This paper presents an adapted review approach,
AML-Review, for multi-disciplinary engineering (MDE) projects
based on best practices for reviews in software engineering.
[Method] Software reviews have been successfully used for early
defect detection in Software Engineering. However, adaptations
are needed for defect detection in MDE environments. We focus
on production systems models according to the emerging
AutomationML standard. [Results] We evaluated the feasibility of
the AML-Review process with requirements and an
AutomationML model from a real-world application scenario.
The AML-Review process provides the benefits of systematic and
traceable review results for MDE projects based on
AutomationML. [Conclusion] The prototype results imply that
systematic and structured review processes help to improve
traceability of requirements and defects and increase defect
detection performance.

Multi-disciplinary Engineering, Automation Systems, Defect Detection, Quality Assurance, Risk Management, Reviews, inspection, AutomationML.