Active learning based requirement mining for cyber-physical systems

This paper uses active learning to solve the problem of mining signal temporal requirements of cyber-physical systems or simply the requirement mining problem. By utilizing robustness degree, we formulate the requirement mining problem as an optimization problem. We then propose a new active learning algorithm called Gaussian Process Adaptive Confidence Bound (GP-ACB) to help in solving the optimization problem. We show theoretically that the GP-ACB algorithm has a lower regret bound-thus a larger convergence rate-than some existing active learning algorithms, such as GP-UCB. We finally illustrate and apply our requirement mining algorithm with two case studies: the Ackley's function and a real world automotive power steering model. Our results demonstrate that there is a principled and efficient way of extracting requirements for complex cyber-physical systems.