Artifact for "CTMCs with Imprecisely Timed Observations"
Artifact for "CTMCs with Imprecisely Timed Observations"
This artifact contains an implementation of the approach from the paper "CTMCs with Imprecisely Timed Observations" presented at TACAS 2024.The implementation is written in Python and builds upon the probabilistic model checker Storm.The artifact contains all benchmarks (CTMC models, properties, and evidences) used in the numerical experiments presented in Section 6 of the paper.The artifact allows to reproduce all the results of the numerical experiments, in particular, Table 2 and Figure 6 (in the main paper) and Figure 8 (in the appendix). The artifact is licensed under GPL-3.0Please see the README within the zip archive on how to obtain, build and run the artifact. Requirements:No special requirements are needed, the artifact works without an internet connection.Building the artifact requires 1-2 hours.Running all experiments requires roughly 4 hours.
- University of Twente Netherlands
- Eindhoven University of Technology Netherlands
- Radboud University Nijmegen Netherlands
- Ruhr University Bochum Germany
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).1 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Citations provided by BIP!Popularity provided by BIP!