Runtime Verification with Particle Filtering

Kenan Kalajdzic, Ezio Bartocci, Scott A. Smolka, Scott D. Stoller, Radu Grosu

Abstract: We introduce Runtime Verification with Particle Filtering (RVPF), a powerful and versatile method for controlling the tradeoff between uncertainty and overhead in runtime verification. Overhead and accuracy are controlled by adjusting the frequency and duration of observation gaps, during which program events are not monitored, and by adjusting the number of particles used in the RVPF algorithm. We succinctly represent the program model, the program monitor, their interaction, and their observations as a dynamic Bayesian network (DBN). Our formulation of RVPF in terms of DBNs is essential for a proper formalization of peek events: low-cost observations of parts of the program state, which are performed probabilistically at the end of observation gaps. Peek events provide information that our algorithm uses to reduce the uncertainty in the monitor state after gaps.