TEAL: Synthesizing Efficiently Monitorable Formulas in Metric Temporal Logic
Creators
- 1. University of Bordeaux
- 2. University of Antwerp
- 3. Max Planck Institute for Software Systems, Kaiserslautern, Germany
- 4. CNRS, LaBRI, Université de Bordeaux, France & The Alan Turing Institute of data science, United Kingdom
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5.
TU Dortmund University
Description
TEAL is an artifact for the VMCAI24 Contribution "Synthesizing Efficiently Monitorable Formulas in Metric Temporal Logic" by Ritam Raha, Rajarshi Roy, Nathanaël Fijalkow, Daniel Neider and Guillermo A. Perez.
TEAL is a Python-based tool for synthesizing formulas in Metric Temporal Logic (MTL) for efficient Runtime monitoring. To synthesize MTL formulas, it relies on solving constraint satisfaction problems using the SMT solver Z3. TEAL is written in Python3.9 and has been tested to work on the following OS: Debian 10, Ubuntu 22.04, and MacOS Ventura 13.6.
The artifact we provide contains TEAL, along with the synthetic benchmarks used for the experiments in the paper. We provide all the necessary instructions to use TEAL and reproduce the experiments used in the paper.
Files
Files
(272.7 kB)
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