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...
In safety-critical software—whether in avionics, automotive systems, medical devices, or industrial automation—the margin for error is essentially zero. Every line of code must exist for a clearly defined purpose, and that purpose must be rooted in an approved requirement. This strict discipline is vital not only for certification, but also for ensuring that the system behaves predictably under all operating conditions. One of the most overlooked sources of defects in such systems is unused or dead code —software elements that do not correspond to any requirement and are not executed during normal operation. While such code may appear harmless, it can introduce significant risks. This is where end-to-end traceability plays a powerful role.