CIL: Intermediate Language and Tools for Analysis and Transformation of C Programs
CC '02 Proceedings of the 11th International Conference on Compiler Construction
Automata-Based Verification of Temporal Properties on Running Programs
Proceedings of the 16th IEEE international conference on Automated software engineering
MiBench: A free, commercially representative embedded benchmark suite
WWC '01 Proceedings of the Workload Characterization, 2001. WWC-4. 2001 IEEE International Workshop
Introduction to Algorithms, Third Edition
Introduction to Algorithms, Third Edition
Comparing LTL Semantics for Runtime Verification
Journal of Logic and Computation
Runtime Verification for LTL and TLTL
ACM Transactions on Software Engineering and Methodology (TOSEM)
Sampling-based runtime verification
FM'11 Proceedings of the 17th international conference on Formal methods
PSL model checking and run-time verification via testers
FM'06 Proceedings of the 14th international conference on Formal Methods
Runtime monitoring of time-sensitive systems
RV'11 Proceedings of the Second international conference on Runtime verification
Runtime verification of real-time embedded systems
Proceedings of the tenth ACM international conference on Embedded software
RiTHM: a tool for enabling time-triggered runtime verification for C programs
Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering
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Time-triggered runtime verification aims at tackling two defects associated with runtime overhead normally incurred in event-triggered approaches: unboundedness and unpredictability . In the time-triggered approach, a monitor runs in parallel with the program and periodically samples the program state to evaluate a set of properties. In our previous work, we showed that to increase the sampling period of the monitor (and hence decrease involvement of the monitor), one can employ auxiliary memory to build a history of state changes between subsequent samples. We also showed that the problem of optimization of the size of history and sampling period is NP-complete. In this paper, we propose a set of heuristics that find near-optimal solutions to the problem. Our experiments show that by employing negligible extra memory at run time, we can solve the optimization problem significantly faster, while maintaining a similar level of overhead as the optimal solution. We conclude from our experiments that the NP-completeness of the optimization problem is not an obstacle when applying time-triggered runtime verification in practice.