We present the OGAN algorithm for automatic requirement falsification of cyber-physical systems. System inputs and output are represented as piecewise constant signals over time while requirements are expressed in signal temporal logic. OGAN can find inputs that are counterexamples for the safety of a system revealing design, software, or hardware defects before the system is taken into operation. The OGAN algorithm works by training a generative machine learning model to produce such counterexamples. It executes tests atomically and does not require any previous model of the system under test. We evaluate OGAN using the ARCH-COMP benchmark problems, and the experimental results show that generative models are a viable method for requirement falsification. OGAN can be applied to new systems with little effort, has few requirements for the system under test, and exhibits state-of-the-art CPS falsification efficiency and effectiveness.

OGAN算法通过训练生成式机器学习模型，可以自动对物理系统的需求进行伪造，为系统的安全性揭示设计、软件或硬件缺陷，并在系统投入运行之前发现。该算法使用原子测试执行，无需先前模型的支持，通过在ARCH-COMP基准问题上的评估结果表明，生成式模型是一种可行的需求伪造方法。OGAN不仅适用于新系统，并且对系统的要求很少，具有最先进的CPS伪造效率和效果。

基于生成模型的网络物理系统需求破解