Volume 62, Issue 11 - Special Issue: Softwaredesign für die Zukunft – geplante und gemanagte Softwareevolution / Birgit Vogel-Heuser, Ralf Reussner

Evaluating research approaches in production automation can be cost- and time-intensive. Motivated by this challenge, the role of a case study which is open to the research community is discussed at first and recent developments towards an open case study, the Pick & Place Unit (PPU), are presented. Based on evolution aspects not covered by the PPU case study until now, an extension on requirements, fault handling and parallel evolution are presented as an example. While carving out essential aspects of these topics, various challenges were identified that conclude this article.

The software constitutes a major part of nowadays automation systems being responsible for conducting complex control tasks. Machines and plants are often unique in some industrial branches; hence, they become mechatronic products configured individually. The inherent software variability of those highly-configurable systems makes efficient, yet accurate quality assurance a challenging task. This article presents a comprehensive approach for applying model-based software product line testing techniques to the automation engineering domain. Existing approaches for variability modeling are adapted to domain specific modeling languages to allow for variability-aware test case generation and execution. The implementation of the approach is evaluated by means of a sample automation system product line.

The concept described in this contribution utilizes available process data in production systems to enable to verify the fulfillment of (non-functional) requirements during operation. Therefore, the contribution presents a systematic approach to automatically derive property values out of signal traces by using adaptable runtime models. Those can be checked for the violation of limit values in order to verify the fulfillment or violation of requirements on these properties during an evolution. In an example application to a Pick and Place unit, the concept is used together with an anomaly detection method to support the operator during the evolution process by constantly providing information regarding requirement fulfillment.

Manufacturing systems and their control software exhibit a large number of variants, which evolve over time in order to meet changing functional and non-functional requirements. To handle the resulting complexity, we propose a multi-perspective modeling approach with different viewpoints regarding workflow, architecture and component behavior. We combine it with delta modeling to seamlessly capture variability and evolution by the same means on each of the viewpoints. We show how the separation in different viewpoints enables early performance analysis as well as code generation. The approach is illustrated using a case study.

Model-based Software Development (MBSD) is a promising approach to successfully manage the evolution of software in industrial plant automation. In MBSD, models are central development artifacts. They often exist in several variants which evolve in parallel, e.g. in different customer's installations. Local improvements during operation and maintenance usually have to be propagated to other variants or back-ported to a central base version. This paper describes a technology which enables developers to propagate changes between models without compromising the consistency of target models.

To facilitate engineering and evolution of automation systems, ensuring the correctness of the design models is an important topic. Industrial automation systems are composed of various heterogeneous elements designed by different disciplines such as mechanical, electrical/electronic and software engineering. In this contribution, an approach for modeling industrial automation systems is presented which is based on interface behavior modeling of design artifacts and which supports automatic verification of their functional conformance while considering information from various disciplines.

Zusammenfassung Erratum to http://dx.doi.org/10.1515/auto-2014-1110.