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Applying AI in Aerospace Software Development Under DO-178C Standards

Applying AI in Aerospace Software Development Under DO-178C Standards
Andrew Longhurst, Managing Director of WITTENSTEIN high integrity systems, offers a system-level perspective on safety-critical aerospace design, combining technical expertise with commercial insight to deliver certifiable solutions. As artificial intelligence (AI) rapidly reshapes software development, its potential applications in aerospace engineering have garnered significant attention. AI promises to accelerate engineering workflows, facilitate earlier exploration of complex designs, and reduce repetitive manual tasks. However, the aerospace industry’s unwavering emphasis on safety, determinism, and certification—embodied in standards such as DO-178C—places stringent constraints on the adoption of AI in software development.
The fundamental question facing the sector is whether AI can be effectively integrated into DO-178C-compliant aerospace software development without compromising the integrity of certification. Unlike commercial software, aerospace code demands comprehensive understanding, justification, and independent verification of every element. This requirement imposes unique challenges on how AI tools are employed throughout the development lifecycle.
AI’s Role and Limitations Within DO-178C Frameworks
To leverage AI effectively in aerospace software development, it is essential to recognize its inherent limitations. AI models, much like human engineers, are prone to errors and “hallucinations,” where they may generate misleading or incorrect information. Consequently, AI is best utilized as an advanced assistant rather than an autonomous creator of certified code or documentation. When applied judiciously, AI can aid in summarizing complex standards, proposing candidate design solutions, and identifying inconsistencies, thereby expediting development without compromising safety or regulatory compliance.
The early phases of development offer the most promising opportunities for AI to add value with minimal certification risk. AI can assist in decomposing high-level system requirements into detailed software requirements, suggest modular architectures that enhance partitioning and redundancy, and identify potential single points of failure. For example, if a requirement states, “The system shall detect sensor failure,” AI can help elaborate detection strategies, timing constraints, and safe-state transitions—supporting DO-178C objectives related to requirements correctness and completeness. Nonetheless, ultimate responsibility for documentation and design decisions remains with human engineers, who must ensure that all AI-generated outputs undergo rigorous independent review.
AI can also generate boilerplate code, including interfaces, state machines, and drivers, across programming languages such as C, C++, Ada, or Rust, promoting consistency and efficiency. From a DO-178C perspective, AI-generated code is subject to the same rigorous scrutiny as human-written code: every line must trace back to a requirement, comply with coding standards, be independently reviewed, and pass deterministic testing. In this context, AI transforms the engineer’s role from author to reviewer, while accountability remains entirely with human professionals.
Verification, often the most resource-intensive phase of aerospace software development, is another area where AI can contribute meaningfully. AI tools may generate unit tests derived from requirements, propose boundary-value scenarios, and analyze logs to identify coverage gaps or anomalies. However, all AI-generated outputs must be independently validated and remain fully explainable to satisfy certification demands.
Industry Challenges and Market Response
The integration of AI into aerospace software development under DO-178C standards presents considerable challenges. Ensuring compliance with stringent safety and certification requirements, integrating AI with legacy systems, and managing the complexity of AI model validation and verification are significant obstacles. Market reactions have been mixed: some investors remain cautious about the disruptive potential of AI, while others view it as a driver of innovation. Industry competitors are responding by accelerating investments in AI capabilities. For instance, Honeywell is expanding its presence in the defense market, and Archer Aviation is establishing new engineering hubs to leverage AI technologies. Despite these initiatives, overall market sentiment remains cautious, with analysts questioning the long-term viability of traditional software businesses amid an increasingly AI-driven landscape.
While AI holds substantial promise for aerospace software development, its integration within the DO-178C framework demands a cautious, professional approach grounded in an unwavering commitment to safety and certification integrity.

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