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Runtime Verification with Particle Filtering

  • Kenan Kalajdzic
  • Ezio Bartocci
  • Scott A. Smolka
  • Scott D. Stoller
  • Radu Grosu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8174)

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.

We estimate the internal state of the DBN using particle filtering (PF) with sequential importance resampling (SIR). PF uses a collection of conceptual particles (random samples) to estimate the probability distribution for the system’s current state: the probability of a state is given by the sum of the importance weights of the particles in that state. After an observed event, each particle chooses a state transition to execute by sampling the DBN’s joint transition probability distribution; particles are then redistributed among the states that best predicted the current observation. SIR exploits the DBN structure and the current observation to reduce the variance of the PF and increase its performance.

We experimentally compare the overhead and accuracy of our RVPF algorithm with two previous approaches to runtime verification with state estimation: an exact algorithm based on the forward algorithm for HMMs, and an approximate version of that algorithm, which uses precomputation to reduce runtime overhead. Our results confim RVPF’s versatility, showing how it can be used to control the tradeoff between execution time and memory usage while, at the same time, being the most accurate of the three algorithms.

Keywords

Hide Markov Model Particle Filter Dynamic Bayesian Network Composite State Forward Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Kenan Kalajdzic
    • 1
  • Ezio Bartocci
    • 1
  • Scott A. Smolka
    • 2
  • Scott D. Stoller
    • 2
  • Radu Grosu
    • 1
    • 2
  1. 1.Faculty of InformaticsVienna University of TechnologyAustria
  2. 2.Department of Computer ScienceStony Brook UniversityUSA

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