Feature: Embedded AI
monitors, can be rigorously analyzed using established tools to detect vulnerabilities or coding standard violations, AI components (e.g., neural networks) introduce new complexities. Emerging tools are beginning to address AI-specific risks, such as unsafe operators in frameworks like TensorFlow or PyTorch, but the field remains in its infancy. Static analysis must therefore evolve to encompass both conventional code and AI framework configurations, ensuring compliance with safety standards like MISRA C/C++ or CERT C/C++.
• Unit testing continues to validate low-level functionality, particularly for non-AI components such as communication protocols, fault handlers and hardware interfaces. However, AI models, which derive behaviour from data rather than explicit code, demand a shift toward data-driven validation. This includes testing for edge cases, adversarial inputs and scenario coverage. While the AI model itself may not be amenable to traditional unit tests, the surrounding code, such as data preprocessing pipelines, inference engines and output handlers, must still undergo rigorous unit testing to isolate and mitigate defects.
• Code coverage metrics, such as modified condition/decision coverage (MC/DC), remain mandatory for certification in domains like aviation (DO-178C) or automotive (ISO 26262). These metrics ensure exhaustive testing of decision logic in conventional code. For AI components, traditional code coverage is not directly applicable, but analogous concepts are emerging. Techniques like input space coverage and neuron activation coverage aim to quantify the sufficiency of testing for neural networks, ensuring that diverse scenarios and model behaviours are exercised during validation.
• Requirements traceability is non-negotiable in safety-critical systems, as it provides an auditable chain from system requirements to implementation and testing. AI complicates this process due to its data-driven, often opaque decision- making. Practices such as explainable AI (XAI), model introspection and thorough documentation of training data, model architecture and validation results are essential in maintaining traceability. For example, a requirement such as “the system shall detect obstacles in low-light conditions” must map not only to
sensor code but also to the AI model’s training data diversity and robustness testing.
• Regulatory frameworks (e.g., IEC 62304 for medical devices) implicitly mandate these practices, regardless of AI involvement. Hybrid approaches that combine classical verification with AI-specific methods, such as adversarial testing, runtime monitoring and bias detection, are necessary to address both traditional software risks (e.g., memory leaks) and AI-specific failure modes (e.g., dataset bias). Furthermore, system-level safety depends on the seamless integration of AI and non-AI components; flaws in either can cascade into catastrophic failures. While AI introduces novel challenges,
traditional verification practices remain indispensable. The solution lies in augmenting these practices with AI-aware techniques, ensuring comprehensive coverage of both code and model risks. By maintaining rigorous static analysis, testing, coverage and traceability whilst adapting tools and methods to address AI’s unique demands, developers can achieve the robustness required for safety- critical certification and operational deployment.
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