Elektronik und Informatik
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Design and Implementation of a Plug-In Repetitive Controller for a High Precision Axis System
(2021)
Software design patterns and the abstractions they offer can support developers and maintainers with program code comprehension. Yet manually-created pattern documentation within code or code-related assets, such as documents or models, can be unreliable, incomplete, and labor-intensive. While various Design Pattern Detection (DPD) techniques have been proposed, industrial adoption of automated DPD remains limited. This paper contributes a hybrid DPD solution approach that leverages a Bayesian network integrating developer expertise via rule-based micropatterns with our machine learning subsystem that utilizes graph embeddings. The prototype shows its feasibility, and the evaluation using three design patterns shows its potential for detecting both design patterns and variations.
Nowadays, businesses with focus on consumer-products are challenged by short production cycles, high pricing pressure, and the need to deliver new features and services in a regular interval. Currently, businesses are tackling these challenges by automating their business pro- cesses, while yet trying to be flexible by introducing methods for process variability modeling. However, for larger processes and variability models, it becomes difficult to consider, maintain, and optimize all process variations in the various execution contexts. In software development, highly agile requirements are usually tackled with a flexible microservice architecture. Nonetheless, the fast-changing service landscape is often not fully reflected in the underlying business processes, leading to inefficiency and loss of profit. With this work, we extend our framework for process variability modeling with concepts of Microflows, allowing agile business process modeling and orchestration while utilizing the full flexibility of underlying microservices. In addition, we present a case study, showing how this approach is used in the context of an IoT application