AutomationML review support in multi-disciplinary engineering environments

Abstract

[Context] 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.