Artificial Intelligence (AI) has transformed the world of technology, enabling systems to learn, adapt, and make decisions without explicit programming. From autonomous vehicles to medical diagnostics and flight control systems, AI promises unprecedented efficiency and capability. However, when it comes to safety-critical systems—where failure could result in injury, loss of life, or significant damage—the use of AI introduces profound challenges that go far beyond traditional software engineering. Unlike conventional software, which behaves predictably according to its programmed logic, AI is built on learning and training. Its decisions and outputs depend heavily on the data it has been trained on and the patterns it recognizes during runtime. This adaptive, data-driven behavior means that an AI system’s responses may vary with changing inputs or environments, often in ways that are not explicitly defined or foreseen by developers. While this flexibility is a strength in many applica...
Large language models (LLMs) and automated code-generation tools (codex-style assistants, program synthesizers, template generators) are rapidly becoming part of everyday software development. They promise dramatic productivity gains: boilerplate code, test scaffolding, parsing logic, and even non-trivial algorithms can be produced in seconds. For safety-critical domains (avionics, automotive, medical, industrial control), that promise raises a central question: can code produced by LLMs be trusted to be safe, secure, and certifiable? The stakes are high. Unlike consumer applications, safety-critical software must satisfy deterministic timing, memory and resource constraints, predictable error handling, and auditability for certification standards (e.g., DO-178C, ISO 26262, IEC 62304). Code that “works” in a demo but embeds subtle undefined behavior, non-deterministic constructs, unsafe memory accesses, timing regressions, or security vulnerabilities can create catastrophic failures. ...