Adaptive Runtime Verification
We present Adaptive Runtime Verification (ARV), a new approach to runtime verification in which overhead control, runtime verification with state estimation, and predictive analysis are synergistically combined. Overhead control maintains the overhead of runtime verification at a specified target level, by enabling and disabling monitoring of events for each monitor instance as needed. In ARV, predictive analysis based on a probabilistic model of the monitored system is used to estimate how likely each monitor instance is to violate a given temporal property in the near future, and these criticality levels are fed to the overhead controllers, which allocate a larger fraction of the target overhead to monitor instances with higher criticality, thereby increasing the probability of violation detection. Since overhead control causes the monitor to miss events, we use Runtime Verification with State Estimation (RVSE) to estimate the probability that a property is satisfied by an incompletely monitored run. A key aspect of the ARV framework is a new algorithm for RVSE that performs the calculations in advance, dramatically reducing the runtime overhead of RVSE, at the cost of introducing some approximation error. We demonstrate the utility of ARV on a significant case study involving runtime monitoring of concurrency errors in the Linux kernel.
KeywordsDiscrete Time Markov Chain Compound State Forward Algorithm Runtime Overhead Hide Markov Model Model
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- 2.Denaro, G., Mariani, L., Pezze, M., Tosi, D.: Adaptive runtime verification for autonomic communication infrastructures. In: Proc. of the International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM), vol. 2, pp. 553–557. IEEE Computer Society (2005)Google Scholar
- 4.Gordon, N., Salmond, D., Smith, A.: Novel approach to nonlinear/non-Gaussian Bayesian state estimation. In: IEEE Proceedings on Radar and Signal Processing, vol. 140, pp. 107–127. IEEE (1993)Google Scholar
- 5.Huang, X., Seyster, J., Callanan, S., Dixit, K., Grosu, R., Smolka, S.A., Stoller, S.D., Zadok, E.: Software monitoring with controllable overhead. International Journal on Software Tools for Technology Transfer (STTT) 14(3), 327–347 (2012)Google Scholar
- 8.Seyster, J., Dixit, K., Huang, X., Grosu, R., Havelund, K., Smolka, S.A., Stoller, S.D., Zadok, E.: InterAspect: Aspect-oriented instrumentation with GCC. Formal Methods in System Design (2012), accepted on condition of minor revisions Google Scholar
- 12.Modak, S.: Linux Test Project (LTP) (2009), http://ltp.sourceforge.net/