# Falsification of Cyber-Physical Systems with Robustness-Guided Black-Box Checking

arXiv: https://arxiv.org/abs/2005.02126 · HTML: https://mlsanity.com/paper/2005.02126/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2020.

## Built on by (most cited first, 7 of 7)

- [Part-X: A Family of Stochastic Algorithms for Search-Based Test Generation with Probabilistic Guarantees](https://mlsanity.com/paper/2110.10729/) (2021)
- [Requirement falsification for cyber-physical systems using generative models](https://mlsanity.com/paper/2310.20493/) (2023)
- [Probabilistic Black-Box Checking via Active MDP Learning](https://mlsanity.com/paper/2308.07930/) (2023)
- [Worrisome Properties of Neural Network Controllers and Their Symbolic Representations](https://mlsanity.com/paper/2307.15456/) (2023)
- [Learning test generators for cyber-physical systems](https://mlsanity.com/paper/2410.03202/) (2024)
- [Data-Driven Falsification of Cyber-Physical Systems](https://mlsanity.com/paper/2505.03863/) (2025)
- [Falsification-driven reinforcement learning for maritime motion planning](https://mlsanity.com/paper/2510.06970/) (2025)

## Abstract (arXiv)

For exhaustive formal verification, industrial-scale cyber-physical systems (CPSs) are often too large and complex, and lightweight alternatives (e.g., monitoring and testing) have attracted the attention of both industrial practitioners and academic researchers. Falsification is one popular testing method of CPSs utilizing stochastic optimization. In state-of-the-art falsification methods, the result of the previous falsification trials is discarded, and we always try to falsify without any prior knowledge. To concisely memorize such prior information on the CPS model and exploit it, we employ Black-box checking (BBC), which is a combination of automata learning and model checking. Moreover, we enhance BBC using the robust semantics of STL formulas, which is the essential gadget in falsification. Our experiment results suggest that our robustness-guided BBC outperforms a state-of-the-art falsification tool.