Advanced Software Safety (ADVSFT)

This course builds upon the skills learned in the Software Safety (SFT) course. It is presumed and highly recommended that the student has achieved an active and current understanding of the importance of software safety in the planning, analyses of architecture, design, and code and the testing of automated systems. The course expands upon those skills or as learned in Software Safety (SFT) and presents opportunities to apply them in class in diverse situations using a small unmanned aerial system (sUAS) that is also weaponized.

Objectives: To develop practicing skills in writing a Request for Proposal (RFP) for a safety critical system and performing analyses. To develop the practical use of a Safety Assessment Report (SAR). To develop practical skills in deriving requirements and functions leading to a Functional Hazard Analysis (FHA) and Requirements Hazard Analysis for unmanned "sense and avoid" subsystem. Discussion of how to compose a safety argument using metrics results of many analyses of NASA autonomous aerial systems software that was performed using open source tools. Building confidence with your developer’s tools, looking into why and what to consider in tools, and how to review your story in your SAR. To develop these skills in a team based working environment.

Who Should Attend: Experienced system managers and engineers, system safety engineers and software engineers who have attended SFT or have a solid understanding of the basic software safety principles. Students who have advanced experience will be able to progress in the course but without experience or having just finished the SFT course may present problems with student progress. Principals for Safety (PFS) or Safety Leads who must present to Review Boards should attend to learn active management. Practicing System Safety and Software Safety Engineers who are lining up a professional trajectory into autonomous systems will be better informed by this course. Future follow-on courses will cover deeper safety and security issues, autonomous systems, and in particular artificial intelligence, machine learning (ML) and deep learning (DL) models, life cycles, and contract issues.