Machine Learning in ASPICE – a term that raises questions
Since the release of Automotive SPICE 4.0, the term Machine Learning increasingly appears in discussions about development processes. Engineers, project managers, and quality leaders often ask the same question:
What exactly does Machine Learning mean in the ASPICE context?
Is it about artificial intelligence inside the vehicle? Is it about learning software systems? Or is the focus actually on how development processes must evolve?
Interestingly, even among experienced professionals there is often no clear and consistent answer.
The obvious interpretation: learning systems inside the vehicle
The most obvious interpretation is that Machine Learning refers to functions implemented in modern vehicles. Typical examples include:
- Advanced Driver Assistance Systems (ADAS)
- camera-based object detection
- sensor fusion
- speech recognition
- automated driving systems
In these areas, machine learning algorithms are indeed widely used. Models are trained, large datasets are processed, and systems improve their performance through statistical learning.
However, this is exactly where traditional development processes begin to face challenges.
Unlike classical embedded software, machine learning systems often rely on trained models and large datasets rather than deterministic code logic.
The real impact: development processes must evolve
Looking deeper, the term Machine Learning within ASPICE discussions often refers less to the system itself and more to the impact on development processes.
When systems are trained with data rather than purely programmed, several aspects of engineering change fundamentally:
- requirements must be defined differently
- test strategies must be expanded
- data management becomes a central engineering activity
- validation approaches must evolve
This means that not only the system learns — the development process must evolve as well.
New questions for engineering and quality management
The introduction of machine learning technologies introduces questions that rarely existed in classical software development:
- How are training datasets validated?
- How can reproducibility of results be ensured?
- How can the behavior of trained models be verified?
- How can non-deterministic systems be tested?
- How are changes to training data evaluated?
These challenges do not only concern software engineers but also involve:
- quality organizations
- project managers
- system architects
- process owners
ASPICE reflects a shift in software engineering
The discussion ultimately reflects a broader shift in the way modern automotive systems are developed.
In the past, most embedded systems were developed using traditional deterministic programming approaches.
Today, however, many systems are strongly data-driven.
This applies not only to automated driving but also to numerous modern vehicle features.
Automotive SPICE therefore increasingly considers these technological developments in its process discussions.
In this context, Machine Learning should not be interpreted as a specific technology requirement but rather as an indication that development processes must adapt to new engineering realities.
The real challenge lies in engineering organizations
The real challenge is therefore not primarily the technology itself but its integration into existing engineering environments.
Organizations today face several key tasks:
- integrating data-driven development into existing processes
- adapting verification and validation strategies
- defining new quality criteria
- building competencies in data-driven systems engineering
In the automotive industry, where safety, traceability, and regulatory compliance play a critical role, this transformation will become one of the most significant engineering challenges of the coming years.
Conclusion
The question “Who needs to learn?” therefore has a surprisingly clear answer.
Not only the vehicle learns. Not only the software learns.
The development process must learn as well.
Machine learning changes how systems are designed, tested, and validated. Automotive SPICE reflects this shift by addressing new engineering questions within process frameworks.
For companies, this primarily means one thing: engineering processes, organizational structures, and competencies must continue to evolve.
Note:
If your organization is addressing topics such as ASPICE-compliant development, modern software engineering, or data-driven systems, DiNC-POSiTiVE supports companies with technical expertise and process-oriented consulting.