Nowadays, the number of software components in modern vehicles, as well as their complexity, is increasing rapidly due to applications such as autonomous driving. Consequently, more powerful computing platforms are employed and software applications are developed in a distributed manner, e.g., by applying the widely established microservice pattern. Solutions that address associated challenges have been developed, e.g., the CARISMA approach. In this paper, we present an approach to describing the topology of in-vehicle applications, including services, deployed based on CARISMA and their relationships in terms of communication, using the Topology and Orchestration Specification for Cloud Applications. This way, a complex CARISMA topology can be described using a model-based approach that relies on abstract, reusable components. This increases the understandability of the modeled topology and enables an automated deployment and management. This is a revised and extended version of our previous work presented at ESOCC 2025.

The automotive domain is shifting to software-centric development to meet regulation, market pressure, and feature velocity. This shift increases embedded systems' complexity and strains testing capacity. Despite relevant standards, a coherent system-testing methodology that spans heterogeneous, legacy-constrained toolchains remains elusive, and practice often depends on individual expertise rather than a systematic strategy. We derive challenges and requirements from a systematic literature review (SLR), complemented by industry experience and practice. We map them to test case specification techniques and testing tools, evaluating their suitability for automotive testing using PRISMA. Our contribution is a curated catalog that supports technique/tool selection and can inform future testing frameworks and improvements. We synthesize nine recurring challenge areas across the life cycle, such as requirements quality and traceability, variability management, and toolchain fragmentation. We then provide a prioritized criteria catalog that recommends model-based planning, interoperable and traceable toolchains, requirements uplift, pragmatic automation and virtualization, targeted AI and formal methods, actionable metrics, and lightweight organizational practices.

Testing functionality in Software-Defined Vehicles is challenging because requirements are written in natural language, specifications combine text, tables, and diagrams, while test assets are scattered across heterogeneous toolchains. We propose the use of modern tools such as Large Language Models and Vision-Language Models to extract signals and behavioral logic to automatically generate Gherkin scenarios, which are then converted into runnable test scripts. The Vehicle Signal Specification (VSS) integration standardizes signal references, supporting portability across subsystems and test benches. The pipeline uses retrieval-augmented generation to preselect candidate VSS signals before mapping. We evaluate the approach on the safety-relevant Child Presence Detection System, executing the generated tests in a virtual environment and on an actual vehicle. Our evaluation covers Gherkin validity, VSS mapping quality, and end-to-end executability. Results show that 32 of 36 requirements (89

Testing embedded software in modern vehicles is challenging due to system complexity, decentralized architectures, and strict safety and performance constraints. In this work, we present an end-to-end, deployment-aware testing pipeline for IoT-based automotive applications. The pipeline combines requirement-driven test and code generation with large language model (LLM) and vision-language model (VLM) assistance, and human-in-the-loop curation to reduce manual effort and improve consistency. Using Eclipse openDuT, it supports flexible, distributed deployment across geographically separated cyber-physical and IoT infrastructures, optimizing for node availability and cross-organizational coordination. For validation, we conduct a case study using a Child Presence Detection System (CPDS), achieving full functional requirement coverage across all 9 requirements and 100

Nowadays, the amount of software in modern cars as well as its complexity increases rapidly due to applications such as autonomous driving. Consequently, more powerful computing platforms are employed and software applications are developed in a distributed manner, e.g., by applying the widely established microservice pattern. Microservices introduce additional complexity and challenges regarding the communication between the individual services. In previous work, we introduced the CARISMA approach, which addresses these challenges and is specifically designed for application in the automotive domain. The main idea of the CARISMA approach is to employ a service mesh architecture that is adapted to the design of future electric/electronic (E/E) architectures. Initially, we focused on supporting gRPC-based inter-service communication. In this paper, we enhance CARISMA's applicability to the automotive domain by enabling communication with SOME/IP-based services. SOME/IP is a protocol for inter-service communication widely adopted within the automotive domain. This allows our approach to be integrated with services running on established automotive platforms. We evaluate our concept by conducting a case study and benchmark based on a prototypical implementation.

This paper introduces a GenAI-driven approach for automated test case generation, leveraging Large Language Models and Vision-Language Models to translate natural language requirements and system diagrams into structured Gherkin test cases. The methodology integrates Vehicle Signal Specification modeling to standardize vehicle signal definitions, improve compatibility across automotive subsystems, and streamline integration with third-party testing tools. Generated test cases are executed within the digital.auto playground, an open and vendor-neutral environment designed to facilitate rapid validation of software-defined vehicle functionalities. We evaluate our approach using the Child Presence Detection System use case, demonstrating substantial reductions in manual test specification effort and rapid execution of generated tests. Despite significant automation, the generation of test cases and test scripts still requires manual intervention due to current limitations in the GenAI pipeline and constraints of the digital.auto platform.

Nowadays, the amount of software in modern cars as well as its complexity increases rapidly due to applications such as autonomous driving. Consequently, more powerful computing platforms are employed and software applications are developed in a distributed manner, e.g., by applying the widely established microservice pattern. Microservices introduce additional complexity regarding the communication between the individual services. Solutions that address these challenges have been developed. One of these solutions that is specifically designed for an application to the automotive domain is the CARISMA approach. The main idea of CARISMA is to employ a service mesh architecture that is adapted to the design of future electric/electronic (E/E) architectures. In this paper, we present an approach to describing a topology of services deployed based on CARISMA including their relationships regarding service-to-service-communication using the Topology and Orchestration Specification for Cloud Applications (TOSCA). This way, a complex CARISMA topology can be described using a model-based approach relying on abstract and reusable components. This increases the understandability of the modeled topology and enables an automated deployment and management. Furthermore, because only desired service-to-service communication paths are established, the complexity of the resulting CARISMA configuration is reduced.

This paper presents a domain-driven design-inspired methodology for modeling semantically meaningful interfaces in automotive architectures, focusing on software-defined vehicles as cyber-physical systems. Using a safety-critical life presence detection use case, the approach systematically identifies bounded contexts, separates domain, application, and infrastructure layers, and derives semantic interface characterizations across abstraction levels. The characterization model enables reasoning about timing, criticality, and platform binding, supporting modularity and cross-domain traceability. The methodology complements standards such as AUTOSAR and SysML by offering a lightweight conceptual framework for early-phase system integration, architectural evolution, and scalable design. Copyright (c) 2025 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

The emergence of software-defined vehicles (SDVs) presents a significant challenge for the role of software in managing complex automotive systems, complicating maintenance and upgrades. The software interfaces between components and systems are key throughout the vehicle which enable communication and allow for data flows in SDVs. As of now, the interfaces in the vehicle are stiff and mostly based on the AUTOSAR standard. Service-oriented architecture (SOA), proven in enterprise and consumer computer systems, is now adopted in automotive to address this complexity. This literature review assesses the current state of SOA in the automotive industry, highlighting solutions to solve the interface challenges. The static interface definitions in AUTOSAR limit adaptability, while nonstandardized approaches offer greater flexibility. We identify a gap in how service interfaces interact with internal architecture and external non-SOA components, hindering seamless software-hardware integration. Realizing SOA's potential requires standardized interface patterns and hardware abstraction layers to integrate services and components better.

The automotive industry continues to grow increasingly complex, driven by innovations in autonomous driving, connectivity, electrification, and the expansion of software-defined vehicle architectures. Ensuring the reliability of automotive systems through testing is safety-critical, particularly for critical cases such as, for example, in-vehicle child presence detection systems (CPDS). We assess the use of the open-source testing framework Eclipse openDuT in the automotive industry precisely on this case. CPDS uses multiple interconnected Electronic Control Units (ECUs), making effective and comprehensive testing a challenge. Eclipse openDuT addresses these complexities by enabling early-stage distributed integration testing and facilitating reliable verification of interconnected ECU functionalities. In this case study, we describe the architecture, implementation, and our experiences using openDuT in a real-world application, providing an initial qualitative evaluation of its potential. Further, we derive research and development directions regarding automotive testing using Eclipse openDuT.

Nowadays, data analysis is widely used in numerous areas to identify new trends, opportunities, or risks and to improve decision-making. In many cases, however, data analysis is only possible by incorporating specific domain knowledge, which is why domain experts need to be involved. To this end, data mashups are a popular tool for modeling tailored analyses. Yet, with today's data volumes from heterogeneous source systems, it is very difficult to identify beneficial data sources, in particular for explorative data analysis. In this paper, we first define requirements aiming for user-centric analytics, followed by the introduction of SDRank, a deep-learning-based approach to identify beneficial data sources. In an extensive evaluation with three scenarios, we show that this approach offers high robustness concerning the training data used and can reliably identify beneficial data sources, even for previously unknown domains, i.e., transfer learning.

Digitalization has transformed many aspects of our lives, particularly in the automotive industry, where it has significantly impacted how cars are built, tested, and operated. Car manufacturers are increasingly required to shift their focus from hardware to software, a change that is accelerated by the rise of electric vehicles. As the industry increasingly embraces a tech company identity, software-based approaches and new technologies can enhance existing processes, reduce costs, and generate revenue. The integration testing of car electronic control units (ECUs) is a prime example of an area that can be optimized through software-based approaches. In the traditional process, suppliers design and create ECUs and their corresponding software in various locations worldwide. Once completed, the units are shipped to a central location where the car manufacturer physically connects them and conducts integration tests. However, this process can lead to expensive iterations because of found errors, which a virtual approach can address. An initial approach towards early integration testing through a globally accessible virtual testing platform is presented in this paper. Suppliers can leverage such a platform to connect their newly built ECUs, allowing car manufacturers to conduct integration tests early in the ECU’s life cycle. This, in turn, reduces the overhead associated with physical transportation and facilitates early feedback, which can help identify and address issues much earlier in the development process. Overall, the proposed approach has great potential to enhance the integration testing process, resulting in significant benefits for all stakeholders involved. The contributions of this paper include an initial approach towards (i) a generic test specification that enables the definition of integration tests involving virtual and physical ECUs distributed among different suppliers, (ii) a hardware interface that enables suppliers to connect their ECUs to a virtual test environment remotely, and (iii) an initial architecture for the virtual test platform, which allows integration testing throughout multiple distributed ECUs.

The amount of software in modern cars is increasing continuously with traditional electric/electronic (E/E) architectures reaching their limit when deploying complex applications, e.g., regarding bandwidth or computational power. To mitigate this situation, more powerful computing platforms are being employed and applications are developed as distributed applications, e.g., involving microservices. Microservices received widespread adoption and changed the way modern applications are developed. However, they also introduce additional complexity regarding inter-service communication. This has led to the emergence of service meshes, a promising approach to cope with this complexity. In this paper, we present an architecture applying the service mesh approach to automotive E/E platforms comprising multiple interlinked High-Performance Computers (HPCs). We validate the feasibility of our approach through a prototypical implementation.

The aim of the 20th IEEE Workshop on Context and Activity Modeling and Recognition (CoMoRea'24) is on the one hand to advance the state of the art in the area of activity modeling and recognition; on the other hand, to investigate the research area of context modeling and reasoning and to address fundamental issues in context processing and management.The goal is to identify concepts, theories and methods applicable to context modeling, context reasoning, and activity recognition, as well as systemoriented issues related to the design and implementation of context-aware systems.CoMoRea illustrates the growing interest in activity recognition and the relationship between context models and context/ activity recognition.The workshop's international program committee refereed the submitted papers based on both their quality and their relevance to the workshop theme.In total, this year's workshop program includes 7 high quality papers that were carefully selected out of 15 submissions, as well as a keynote by Claudio Bettini.We would like to thank the members of the program committee for providing detailed and thoughtful reviews, and thus making it possible to assemble an interesting program of high quality papers.We would also like to thank the PerCom organizers, especially the PerCom Workshops co-chairs for supporting the workshop and assisting with its organization.Finally, we thank all of the authors and workshop attendees for their contributions and participation.

Nowadays, the software complexity of modern cars increases rapidly due to highly sophisticated applications, such as autonomous driving. Consequently, traditional electric/electronic (E/E) architectures are increasingly reaching their limit when deploying such complex applications, e.g., regarding bandwidth or computational power. To cope with this issue, multiple High-Performance Computers are introduced to modern car infrastructures and applications are developed in a distributed manner, e.g., involving microservices. Microservices are widely established and changed the way modern applications are developed. Yet, they introduce additional complexity regarding communication between services. A potential solution for this complexity are service meshes. In our previous work, we introduced CARISMA - a first approach of an architecture applying service meshes to automotive E/E platforms comprising multiple interlinked High-Performance Computers. In this revised and extended version of our previous work, we now extend the initial CARISMA concept and conduct a thorough quantitative evaluation to emphasize its benefits.

The increasing connectivity and data exchange between vehicles and the cloud have led to growing privacy concerns. To keep on gaining product insights through data collection while guaranteeing privacy protection, an anonymization-by-design approach should be used. A rising number of anonymization methods, not limited to the automotive domain, can be found in the literature and practice. The developers need support to select the suitable anonymization technique. To this end, we make the following two contributions: 1) We apply our knowledge from the automotive domain to outline the usage of qualitative metrics for anonymization techniques assessment; 2) We introduce HySAAD, a hybrid selection approach for anonymization by design that leverages this groundwork by recommending appropriate anonymization techniques for each mobile data analytics use case based on both, qualitative (i.e., "soft") metrics and quantitative (i.e., "hard") metrics. Using a real-world use case from the automotive, we demonstrate the applicability and effectiveness of HySAAD.

The volume of data to be analyzed has increased tremendously in recent years. In order to extract knowledge from this data, domain experts gain new insights with the help of graphical analysis tools for explorative analyses. Here, the reliability and trustworthiness of an exploratory analysis is determined by the quality of the underlying data. Existing approaches require manual testing to ensure data quality which is often neglected. This research aims to introduce a novel interactive approach for seamlessly integrating data quality considerations into the process of explorative data analysis conducted by domain experts. We derive requirements, conduct an extensive literature review, and develop an approach that efficiently combines stakeholders’ strengths, allowing unobtrusive data quality integration in interactive analysis. Our approach enhances trustworthiness due to unobtrusive monitoring of data quality within the context of explorative data analysis. Domain experts gain insights more reliably, bridging the gap between technical requirements and domain expertise. In conclusion, our research presents a promising solution for improving the reliability and trustworthiness of explorative data analysis, especially for domain experts who may lack technical knowledge. By seamlessly integrating data quality into the analytical process, we empower domain experts to extract valuable insights from the ever-increasing volume of data, thereby advancing the field of data-driven decision-making.