Modernizing the ASPICE Software Engineering Base Practices Framework: Integrating Alternative Technologies for Agile Automotive Software Development

Satyajit Lingras; Aruni Basu

Abstract

The automotive industry's reliance on software has grown exponentially in recent years, with advanced functionalities like autonomous driving, vehicle-to-everything (V2X) communication, and real-time data analytics becoming standard. This transformation has led to a dramatic increase in lines of code per vehicle, from around 10 million a decade ago to over 100 million in high-end models today. These advancements bring unique challenges, particularly in managing the complexity, maintaining traceability, and ensuring compliance with safety-critical standards.

This paper proposes strategies to modernize the ASPICE (Automotive SPICE) framework, which provides a robust process assessment model for software development in the automotive industry. The focus is on ASPICE's key process areas (SWE.1 to SWE.5): Requirements Engineering, Architectural Design, Software Unit Design and Implementation, Software Integration, and Software Verification. The authors analyze the existing limitations of traditional ASPICE implementation in agile environments and propose practical solutions leveraging modern technologies, such as blockchain, CI/CD pipelines, and microservices, to achieve both agile velocity and ASPICE compliance.

The paper explores how these modernizations can enhance efficiency, traceability, and quality in automotive software development. The proposed strategies include the use of blockchain for requirements traceability, AI-powered requirements analysis and test case generation, microservices architecture for improved modularity, and the integration of DevOps practices and CI/CD pipelines. The authors also discuss the implications of these modernizations for the automotive industry, highlighting the potential to develop safer, more reliable, and more innovative software-driven vehicle systems.

Keywords

Automotive software developmentAutomotive SPICE (ASPICE)Software engineering processes (SWE.1 to SWE.5)Functional SafetyAgile methodologiesMicroservices architectureShift-left methodologiesArchitectural designBlockchainContinuous integration and deployment (CI/CD)TraceabilityDi

References

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[refR-1] Cara Navas, N. (2020). Automotive SPICE compliance in an Agile Software Development Process A case study on optimization of the work products.Google Scholar ↗

[refR-2] da Cunha Castro, R. (2016). Automotive HMI: Management of Product Development Using Agile Framework (Master's thesis, Universidade do Minho (Portugal)).Google Scholar ↗

[refR-3] Bauer, T., Barkowski, D., Bachorek, A., & Morgenstern, A. (2022). Reference Architectures for Automotive Software. In Reference Architectures for Critical Domains: Industrial Uses and Impacts (pp. 73-111). Cham: Springer International Publishing.Google Scholar ↗

[refR-4] Castro, R. D. C. (2016). Automotive HMI: Management of product development using Agile framework (Doctoral dissertation).Google Scholar ↗

[refR-5] Winz, T., Streubel, S., Tancau, C., & Dhone, S. (2020). Clean A-SPICE Processes and Agile Methods Are the Key to Modern Automotive Software Engineering: Improvement Case Study Paper for EuroSPI 2020 Keynote of Marelli Automotive Lighting. In Systems, Software and Services Process Improvement: 27th European Conference, EuroSPI 2020, Düsseldorf, Germany, September 9–11, 2020, Proceedings 27 (pp. 571-586). Springer International Publishing.Google Scholar ↗

[refR-6] Goswami, P. (2024). The Software-defined Vehicle and Its Engineering Evolution: Balancing Issues and Challenges in a New Paradigm of Product Development.Google Scholar ↗

[refR-7] Pathrose, P. (2024). ADAS and Automated Driving: Systems Engineering. SAE International.Google Scholar ↗

[refR-8] Schlager, C., Macher, G., Messnarz, R., Ekert, D., & Brenner, E. (2023, October). Requirements for Work Products for ASPICE and Cybersecurity. In Proceedings of the Future Technologies Conference (pp. 419-432). Cham: Springer Nature Switzerland.Google Scholar ↗

[refR-9] de Sousa Barbosa, M. I. (2023). The digitalisation of quality-related data in an Automotive Engineering Services Company.Google Scholar ↗

[refR-10] Blanco, D. F., Le Mouël, F., Lin, T., & Escudié, M. P. (2023). A comprehensive survey on Software as a Service (SaaS) transformation for the automotive systems. IEEE Access.Google Scholar ↗

[refR-11] Liu, Y., Xiong, W., & Cheng, T. C. E. (2024). Application of Big-Data Analysis and QFD Based Quality Management Model on Powertrain Electronic Control Software Development. IEEE Engineering Management Review.Google Scholar ↗

[refR-12] Münch, T. System Architecture Design and Platform Development Strategies.Google Scholar ↗

[refR-13] Moukahal, L. J., Elsayed, M. A., & Zulkernine, M. (2020). Vehicle software engineering (VSE): Research and practice. IEEE Internet of Things Journal, 7(10), 10137-10149.Google Scholar ↗

[refR-14] Bernholdt, D. E., Cary, J., Heroux, M. A., & McInnes, L. C. (2021). Position papers for the ASCR workshop on the science of scientific-software development and use. US Department of Energy (USDOE), Washington DC (United States). Office of Science.Google Scholar ↗

[refR-15] Mehrle, P. T. (2020). Exploring the Collaborative Integration of Service Providers in the New Product Development Process of Automobile Manufacturers (Doctoral dissertation, University of Gloucestershire).Google Scholar ↗

[refR-16] Dreves, R., Mayer, R., & Sechser, B. (2022, August). Challenges with Multi-PAM SPICE Assessments. In European Conference on Software Process Improvement (pp. 271-291). Cham: Springer International Publishing.Google Scholar ↗

[refR-17] Henle, J., Otten, S., & Sax, E. (2024, November). Systems Engineering Approach for Compliant Over-the-Air Update Development. In 2024 IEEE International Conference on Recent Advances in Systems Science and Engineering (RASSE) (pp. 1-9). IEEE.Google Scholar ↗

[refR-18] Münch, T. (2022). System Architecture Design and Platform Development Strategies: An Introduction to Electronic Systems Development in the Age of AI, Agile Development, and Organizational Change. Springer Nature.Google Scholar ↗

[refR-19] Risikko, T. (2020). Challenges of adopting DevOps in automotive software development (Bachelor's thesis, T. Risikko).Google Scholar ↗

[refR-20] Holtmann, J., Liebel, G., & Steghöfer, J. P. (2024). Processes, methods, and tools in model-based engineering—A qualitative multiple-case study. Journal of Systems and Software, 210, 111943.Google Scholar ↗

[refR-21] Chatterjee, P. (2022). Machine Learning Algorithms in Fraud Detection and Prevention. Eastern-European Journal of Engineering and Technology, 1(1), 15-27.Google Scholar ↗

[refR-22] Chatterjee, P. (2023). Optimizing Payment Gateways with AI: Reducing Latency and Enhancing Security. Baltic Journal of Engineering and Technology, 2(1), 1-10.Google Scholar ↗

[refR-23] Maro, S., Steghöfer, J. P., & Staron, M. (2018). Software traceability in the automotive domain: Challenges and solutions. Journal of Systems and Software, 141, 85-110.Google Scholar ↗

[refR-24] Chatterjee, P. (2022). AI-Powered Real-Time Analytics for Cross-Border Payment Systems. Eastern-European Journal of Engineering and Technology, 1(1), 1-14.Google Scholar ↗

[refR-25] Heidrich, J., Kläs, M., Morgenstern, A., Antonino, P. O., Trendowicz, A., Quante, J., & Grundler, T. (2021). From complexity measurement to holistic quality evaluation for automotive software development. arXiv preprint arXiv:2110.14301.Google Scholar ↗