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Experiments Data for CAV 2019

This is a document on the experiment data for the CAV 2019 paper [WAH19]. We implemented and evaluated our tool SyMon. We compiled SyMon using GCC 7.3.0. See also README.md.

Experimental raw data

We conducted the experiments on an Amazon EC2 c4.large instance (2.9 GHz Intel Xeon E5-2666 v3, 2 vCPUs, and 3.75 GiB RAM) that runs Ubuntu 18.04 LTS (64 bit). We compiled PPL [BHZ08] with the following options.

--disable-dependency-tracking --enable-coefficients=checked-int64 --disable-silent-rules --enable-optimization=speed

The DOT and signature files are in this directory. The Evaluation data is available from the following link.

Description of the Benchmarks

Copy

Benchmark Copy is inspired by the monitoring of variable updates much like the scenario in [BDSV14]. The action is update and update has one string and one integer values. The string value is the name of the updated variable and the integer value is the updated value.

Dominant

Benchmark Dominant is inspired by the monitoring of withdrawals from bank accounts of various users much like the scenario in [BKZ17]. The action is withdraw and withdraw has one string and one integer values. The string value is the user name and the integer value is the amount of the withdrawals.

Periodic

Benchmark Periodic is inspired by a parameter identification of periodic withdrawals from one bank account. The action is withdraw and withdraw has one integer value. The the integer value is the amount of the withdrawals.

Calling SyMon

For all case studies, the following command was used for PTPM:

./build/symon -pf [ptda.dot] < [timed_data_word.txt]

References

[WAH19]
Masaki Waga, Étienne André, and Ichiro Hasuo, Symbolic Monitoring against Specifications Parametric in Time and Data, Proc. CAV 2019, LNCS 11561, pp. 520-539.
[BDSV14]
Brim, L., Dluhoš, P., Šafránek, D., & Vejpustek, T. (2014). STL⁎: Extending signal temporal logic with signal-value freezing operator. Information and Computation, 236, 52-67.
[BKZ17]
Basin, D. A., Klaedtke, F., & Zalinescu, E. (2017, December). The MonPoly Monitoring Tool. In RV-CuBES (pp. 19-28).