一句话：SAFA 利用深度学习生成需求、设计、代码与测试等安全论证树，自动提示缺失工件并可视化跨版本变更对系统安全的影响。
安全关键系统（如医疗设备、自动驾驶和工厂机器人）在敏捷迭代中发布新功能可能引入危害，导致运行时失效，进而使项目陷入“big freeze”。SAFA 面向演化软件系统中的安全分析，利用深度学习从需求、设计、代码、测试等制品生成安全论证树，展示危害如何被缓解，并在关键制品缺失时自动告警。工具结合颜色、标注和推荐动态可视化不同软件版本间的变更，并在安全案例上增加可视注解，帮助用户检测和分析变更对系统安全的潜在不利影响。作者提供了工具演示视频。摘要未报告定量评估，仅提及工具演示链接。
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 run-time 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. Through the use of cutting-edge deep-learning solutions, it generates trees of requirements, designs, code, tests, and other artifacts that visually depict how hazards are mitigated in the system, and it automatically warns the user when key artifacts are missing. It also 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.
@inproceedings{paperbot7366,
title = {SAFA: A Tool for Supporting Safety Analysis in Evolving Software Systems},
author = {Alberto D. Rodriguez and Timothy Newman and Katherine R. Dearstyne and Jane Cleland-Huang},
booktitle = {ASE},
year = {2022},
doi = {10.1145/3551349.3559535}
}