Precisely modeling complex systems like cyber-physical systems is challenging, which often renders model-based system verification techniques like model checking infeasible. To overcome this challenge, we propose a method called LAR to automatically ‘verify’ such complex systems through a combination of learning, abstraction and refinement from a set of system log traces. We assume that log traces and sampling frequency are adequate to capture ‘enough’ behaviour of the system. Given a safety property and the concrete system log traces as input, LAR automatically learns and refines system models, and produces two kinds of outputs. One is a counterexample with a bounded probability of being spurious. The other is a probabilistic model based on which the given property is ‘verified’. The model can be viewed as a proof obligation, i.e., the property is verified if the model is correct. It can also be used for subsequent system analysis activities like runtime monitoring or model-based testing. Our method has been implemented as a self-contained software toolkit. The evaluation on multiple benchmark systems as well as a real-world water treatment system shows promising results.

中文翻译：

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通过学习、抽象和细化自动“验证”离散时间复杂系统

对网络物理系统等复杂系统进行精确建模具有挑战性，这通常会使基于模型的系统验证技术（如模型检查）变得不可行。为了克服这一挑战，我们提出了一种称为 LAR 的方法，通过从一组系统日志跟踪中学习、抽象和细化相结合，自动“验证”此类复杂系统。我们假设日志跟踪和采样频率足以捕获系统的“足够”行为。给定一个安全属性和具体的系统日志轨迹作为输入，LAR 自动学习和细化系统模型，并产生两种输出。一个是虚假概率有界的反例。另一种是概率模型，基于该模型“验证”给定的属性。该模型可以被视为一种证明义务，即，如果模型正确，则验证属性。它还可以用于后续的系统分析活动，如运行时监控或基于模型的测试。我们的方法已作为一个独立的软件工具包实现。对多个基准系统以及真实世界的水处理系统的评估显示出有希望的结果。