The rapid evolution of modern automotive systems—powered by advancements in autonomous driving, and connected vehicle technologies— pose fundamental challenges to design and integration. Ensuring the safety of these highly interconnected, software-driven systems, with the widespread reuse of components across platforms. This growing complexity calls for a more structured and rigorous approach to safety assurance than traditional methods.
Traditional safety cases tend to take a linear, justification-focused approach that mainly focuses on positive assertions —statements of compliance or success—while giving limited attention to potential weaknesses, or gaps in supporting evidence. This practice may lead to criticism that such arguments are “too positive,” portraying an overly optimistic view of system safety without sufficiently acknowledging areas of unresolved risk. As a result, conventional approaches for developing safety case may overlook complex interactions, assumptions, and uncertainties that require critical examination, not default acceptance.
As opposed to traditional methods of developing safety case through justification, the dialectic approach emphasizes critical analysis and scrutiny of weak points using counterarguments, alternative perspectives, and open challenges. It encourages a deliberate effort to explore not just what works in a design, but what might fail— anticipating negative aspects, design vulnerabilities, and areas where safety assumptions may fail. Rather than simply validating assumptions, it aims to uncover hidden flaws, inconsistencies, and evidence gaps that could compromise system safety. By applying this time-tested principle to safety assurance, the dialectic method transforms the safety case into a living, questioning tool that evolves with improving system understanding—becoming increasingly transparent, robust, and credible.
In the paper, we will be demonstrating 3SK’s practical application of the dialectic methodology for developing safety case. By this approach, we were able to pick out important safety gaps that would otherwise have gone unnoticed, hence enhancing the completeness, credibility, and robustness of our safety assurance practices.