The integration of digital twins, i.e., virtual replicas of physical systems, is increasingly transforming manufacturing by enhancing efficiency through real-time monitoring, simulation, and optimization. The 3D-visualization of their data as a core functionality of xR applications (e.g., Virtual Reality) extends their usefulness and can be used as an important tool for teaching, training, and support. However, in addition to already known and well-discussed challenges (e.g., data representation), developing digital twin-assisted xR applications poses various variability challenges due to the complexity of manufacturing processes, data models, and the need for configurability across various scenarios and platforms. In this paper, we share our experiences in developing such applications, focusing on the gap of handling variability. Based on the DigiLehR research project, which also includes three industrial use cases as configurable products of an xR application family, we describe challenges we faced during development and essential lessons learned. Here, we particularly focus on platform specifics, immersion and interaction, digital twin-related data fragmentation, accessibility, and security. Overall, our work aims to create awareness for practitioners and researchers about the challenges of developing digital twin-assisted xR applications and their configurations, encouraging discussions on their efficient application in industrial settings.
Software product line engineering (SPLE) enables systematic reuse in developing various products within specific domains. Several approaches have been proposed to support SPLE implementation. In this paper, we report our experiences using delta-oriented programming (DOP) to implement a payment gateway product line. The implementation has specific challenges because the services of payment gateway are provided by many vendors. Even though they offer similar core services, each vendor often has distinct characteristics that must be accommodated in the product line application. The core services are modeled as similar features in the feature model, but each vendor's implementation might differ. Therefore, the feature selection must address vendor-specific configurations, requiring a multi-level configuration. This paper discusses the challenges and proposes solutions for achieving a multi-level configuration in the payment gateway domain. We also implement the solution as a tool support to enhance flexibility and promote applicability across other domains.
Uniform random sampling (URS) has many applications in configurable systems analysis. Usually, feature models consisting of a hierarchical feature diagram and additional side constraints specify the space of valid configurations to be sampled from. However, URS has predominately been applied on feature models translated a priori into conjunctive normal form (CNF). In this work, we study URS approaches that instead operate directly on feature diagrams and provide a comparative evaluation of their performance against well-established URS tools for CNF representations. Our findings suggest that translating feature models to CNF offers advantages, even in the presence of only few side constraints.
Behavioral Programming is a paradigm that aims to enable incremental development by utilizing scenarios and use cases for actual software development. The overall behavior is formed by the composition of individual threads (b-threads) that run concurrently with each other and use an event-based mechanism to communicate and effect the system's state. However, with a rising number of threads, it gets progressively harder to comprehend state changes and system architecture, making design decisions more difficult. This holds in particular for context-aware programs, i.e., systems that need to react to contextual changes. In this paper, we propose the idea of employing feature modeling techniques from variability management to achieve an architectural representation of context-aware behavioral programs, enabling us to model the relations between behavioral threads and facilitating techniques from context-aware variability management. We illustrate multiple potential advantages and apply the approach to two examples from literature.
Cloud-based software systems offer numerous configuration options which significantly increase the complexity of variability management. The TOSCA DSL has been defined to reduce the reliance on specific vendors and to enhance the interoperability across different cloud services. This approach, however, does not support systematic variability management. Our paper thus introduces an approach addressing the challenges of supporting different types of configurations across different binding times in cloud-based systems. Specifically, by employing TOSCA's vendor-neutral definition language, our approach standardizes and simplifies the management of cloud configurations. We evaluate the correctness and performance of our approach for different configuration dimensions based on a cloud-based voting application. Our approach can improve the flexibility and efficiency of managing cloud environments, which is essential for robust and effective cloud operations.
