Runtime verification with state estimation

  • Authors:

  • Scott D. Stoller;Ezio Bartocci;Justin Seyster;Radu Grosu;Klaus Havelund;Scott A. Smolka;Erez Zadok

  • Affiliations:

  • Department of Computer Science, Stony Brook University;Department of Applied Math and Statistics, Stony Brook University;Department of Computer Science, Stony Brook University;Department of Computer Science, Stony Brook University;Jet Propulsion Laboratory, California Institute of Technology;Department of Computer Science, Stony Brook University;Department of Computer Science, Stony Brook University

  • Venue:
  • RV'11 Proceedings of the Second international conference on Runtime verification
  • Year:
  • 2011

Quantified Score

Hi-index 0.00

Visualization

Abstract

We introduce the concept of Runtime Verification with State Estimation and show how this concept can be applied to estimate the probability that a temporal property is satisfied by a run of a program when monitoring overhead is reduced by sampling. In such situations, there may be gaps in the observed program executions, thus making accurate estimation challenging. To deal with the effects of sampling on runtime verification, we view event sequences as observation sequences of a Hidden Markov Model (HMM), use an HMM model of the monitored program to "fill in" sampling-induced gaps in observation sequences, and extend the classic forward algorithm for HMM state estimation (which determines the probability of a state sequence, given an observation sequence) to compute the probability that the property is satisfied by an execution of the program. To validate our approach, we present a case study based on the mission software for a Mars rover. The results of our case study demonstrate high prediction accuracy for the probabilities computed by our algorithm. They also show that our technique is much more accurate than simply evaluating the temporal property on the given observation sequences, ignoring the gaps.