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FalCAuN

FalCAuN is a toolkit for testing black-box systems (e.g., cyber-physical systems) based on automata learning and model checking. Currently, systems implemented in Java and Simulink are supported.

FalCAuN is implemented in Java and can be used from any JVM languages including Kotlin and Clojure. The source code is partially commented on using the Javadoc syntax. The document is hosted on GitHub Pages. FalCAuN uses LearnLib, an open source framework for automata learning. It is available under the GNU General Public License Version 3.0.

Contributors

The development of FalCAuN has been led by Masaki Waga. The following people contributed to FalCAuN.

  • Masaki Waga: 2019--
  • Junya Shijubo: 2021--2022
  • Hiromasa Saito: 2024--

References

  • [Shijubo+, RV'21] Efficient Black-Box Checking via Model Checking with Strengthened Specifications. Junya Shijubo, Masaki Waga, and Kohei Suenaga
  • [Waga, HSCC'20] Falsification of cyber-physical systems with robustness-guided black-box checking. Masaki Waga