Johannes Stümpfle

Abstract

Software-defined vehicles (SDVs) offer a wide range of connected functionalities, including enhanced driving behavior and fleet management. These features are continuously updated via over-the-air (OTA) mechanisms, resulting in a growing number of software versions and variants due to the diversity of vehicles, cloud/edge environments, and stakeholders involved. The lack of a unified integration environment further complicates development, as connected mobility solutions are often built in isolation. To ensure reliable operations across heterogeneous systems, a dynamic orchestration of functions that considers hardware and software variability is essential. This paper presents an open-source CI/CD pipeline tailored for SDVs. It automates the build, test, and deployment phases using a combination of containerized open-source tools, creating a standardized, portable, and scalable ecosystem accessible to all stakeholders. Additionally, a custom OTA middleware distributes software updates and supports rollbacks across vehicles and backend services. Update variants are derived based on deployment target dependencies and hardware configurations. The pipeline also supports continuous development and deployment of AI models for autonomous driving features. Its effectiveness is evaluated using an automated valet parking (AVP) scenario involving TurtleBots and a coordinating backend server. Two object detection variants are developed and deployed to match hardware-specific requirements. Results demonstrate seamless OTA updates, correct variant selection, and successful orchestration across all targets. Overall, the proposed pipeline provides a scalable and efficient solution for managing software variants and OTA updates in SDVs, contributing to the advancement of future mobility technologies.

Abstract

The automotive industry is rapidly shifting towards software-defined vehicles (SDVs), with automotive operating systems becoming central to system integration. Android Automotive Operating System (AAOS) has gained significant adoption from manufacturers due to its developer ecosystem, Over-the-Air capabilities, and Hardware Abstraction Layer that decouples software from hardware. Despite its growing industry presence, a gap exists in academic research examining AAOS's role in the SDV transformation. This paper provides a technical assessment of AAOS through code analysis, architecture review, and implementation on an intelligent cockpit platform. We examine AAOS's potential beyond infotainment to more integral roles in automotive architecture, identifying both strengths—software flexibility, scalability, and cloud integration—and limitations—realtime performance constraints, Google ecosystem dependency, and safety-critical function capabilities. Our contributions include a systematic architecture review focused on HAL implementation, identification of technical limitations for broader automotive applications, documentation of implementation challenges through our case study, and extended architecture components addressing identified limitations. This research informs both academic discourse and industry practice regarding AAOS's role in the evolution towards fully software-defined vehicles.

Abstract

The emergence of Software-Defined Vehicles (SDVs) represents a fundamental shift in automotive design, prioritizing software-centric architectures over traditional hardware-driven models. SDVs require modularity, interoperability, real-time processing, and over-the-air update capabilities throughout the vehicle lifecycle. However, current vehicle systems, characterized by tightly coupled software and hardware, struggle to meet these demands due to their complexity and heterogeneity. A critical first step toward enabling SDVs is the decoupling of software from hardware, which can be facilitated through a robust Hardware Abstraction Layer (HAL). While existing HALs offer hardware independence and standardized interfaces, their applicability and effectiveness in SDV contexts, characterized by multi-vendor ecosystems, safety-critical requirements, and cloud-edge usage, remain uncertain. This paper systematically evaluates current automotive HALs and explores HAL mechanisms from non-automotive domains—including smartphones, networking, and industrial automation—to extract cross-domain insights relevant to SDV development. A criteria-driven evaluation framework is developed to assess HALs against SDV-specific needs. Findings reveal that while middleware-based HALs offer portability and modularity, hypervisor-based approaches better support safety, OTA readiness, and hardware efficiency. Limitations in both approaches are identified, prompting recommendations for a hybrid HAL design that integrates hypervisor isolation with middleware standardization. This paper contributes to the ongoing developments on automotive software architecture by offering insights into the applicability and effectiveness of current HAL strategies. It provides actionable guidance for designing flexible, scalable, and future-ready HALs to support SDVs across their lifecycle. The study supports industry-wide standardization efforts and aims to ensure long-term adaptability of automotive platforms in a connected and rapidly evolving mobility ecosystem.

Abstract

Software systems often evolve into multiple variants to meet diverse requirements. This is usually achieved with the clone-and-own approach, where an existing variant is copied and modified. While efficient in the short term, this approach presents challenges for long-term maintenance. A suitable solution to overcome this, is to re-engineer the variants into a software product line (SPL). However, this process is labor-intensive and prone to errors. Although initial studies explore the use of large language models (LLMs) to assist in the re-engineering tasks, they do not address challenges such as hallucination and limited context windows, which restricts the applicability.In this paper, we present a novel approach to assist the transformation of cloned software variants into an SPL using an LLM. To mitigate hallucination, we propose a self-refinement feedback loop to validate the generated SPL. Additionally, we introduce a variation point filtering technique that reduces the input size, while preserving essential information. To quantify and evaluate the generated output, we propose the use of existing metrics that can be employed for the evaluation. Our evaluation demonstrates the effectiveness of the self-refinement feedback loop and variation point filtering based on an existing case study. The results, benchmarked against the proposed variability metrics, indicate that the generated SPL maintains equivalent complexity and potential for reusability, to the system it is compared against.

Abstract

Software plays an increasingly important role in modern technical systems. For the broad applicability and to appeal to a wide range of customers, software often provides an enormous degree of configurability. In addition, software is under continuous development, further increasing complexity and introducing additional configuration options. Software testers face significant challenges in this context. While product-wise testing of all possible software variants is no longer feasible, alternative testing approaches that are both efficient and effective in detecting potential software faults are required. Here, t-wise interaction sampling is established as a systematic testing approach to ensure coverage of the configuration space. However, current sampling approaches suffer from severe scalability issues, often requiring hours or even days of computation when applied to large-scale Software Product Lines (SPLs). In this paper, we present a novel technique for pairwise sampling and are the first to address the enormous computational effort required to sample large-scale SPLs with the parallel computing capabilities offered by consumer graphics processing units (GPUs). We build upon techniques used in existing sampling approaches, such as SAT solvers and local search strategies, extending these traditionally sequential techniques for parallel execution. Furthermore, we present a novel, highly efficient sample optimization strategy based on Quadratic Integer Programming (QIP) as an advancement. We evaluate our sampling approach SAMParallel on a diverse set of 20 medium- and large-scale configurable software systems from the existing literature. We show that SAMParallel can generate competitive pairwise covering samples with reliably smaller sizes compared to current sampling approaches, while considerably reducing the time investment previously required. Beyond the practical implications of our contribution regarding faster sampling, the presented technique offers various hyper-parameters to further balance sampling times and sample sizes. As a result, software testers can adapt the sampling process to their specific needs.

Abstract

The automotive industry is undergoing an exten- sive transition from hardware-centric development to software- defined vehicles (SDVs). This paradigm shift requires innovative technical solutions and fundamentally different approaches for vehicle design. Together, these developments introduce substantial challenges for an entire industry, affecting both development processes and operations. Although SDVs have attracted con- siderable attention and widespread industry involvement, prior research has primarily emphasized systematic literature reviews identifying emerging academic trends. Consequently, little at- tention has been paid to industry and economic developments that influence how SDV technologies evolve in practice and are crucial for future advancement. In response, this paper provides a different, industry-oriented view of the current trends and associated challenges of SDVs. To achieve this, we conducted a comprehensive analysis of the SDV from an industry-based perspective. We analyzed three sources of information to identify the current trends in SDV development: expert interviews with automotive IT specialists, a study on major automotive trade fairs and workshops, and insights gained from the authors’ professional experience. Based upon, we derive specific challenges arising from these trends. Our findings offer a comprehensive

Abstract

Software-intensive systems emerge in a multitude of variations to meet diverse customer requirements. To develop such variant-rich software systems, software product line (SPL) Engineering has emerged as a key strategy for managing the variability. However, the adoption of SPLs is highly complex due to the diversity of feature model formats and specifications, and the complexity of implementing variability. Leveraging the capabilities of powerful large language models (LLMs) can facilitate the adoption of SPLs. Nonetheless, these LLMs often lack knowledge of the various specifications of potential feature models as well as efficient implementation of different variability mechanisms. To address these challenges, we propose a novel method based on retrieval-augmented generation. This method generates reusable artefacts and a corresponding feature mapping based on a given feature model, thereby aiding system engineers in adopting an SPL.