Many organizations seek to increase their agility in order to deliver more timely and competitive products. However, in safety-critical systems such as medical devices, autonomous vehicles, or factory floor robots, the release of new features has the potential to introduce hazards that potentially lead to runtime failures that impact software safety. As a result, many projects suffer from a phenomenon referred to as the `big freeze’. SAFA is designed to address this challenge. It uses cutting-edge deep-learning solutions to generate trees of requirements, design, code, tests, and other artifacts, in order to visually depict how hazards are mitigated in the system, and then generates warnings when key artifacts are missing. It uses a combination of colors, annotations, and recommendations to dynamically visualize change across software versions, and augments safety cases with visual annotations to aid users in detecting and analyzing potentially adverse impacts of change upon system safety. A link to our tool demo can be found at https://www.youtube.com/watch?v=r-CwxerbSVA.

Alberto D. Rodriguez

University of Notre Dame

Timothy Newman

University of Notre Dame

Katherine R. Dearstyne

University of Notre Dame

Jane Cleland-Huang

University of Notre Dame

United States