# Online GANs for Automatic Performance Testing

arXiv: https://arxiv.org/abs/2104.11069 · HTML: https://mlsanity.com/paper/2104.11069/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 19 references that are in the map.
- First posted on arXiv 21 Apr 2021.

## Builds on (most cited first, 2 of 2)

- [NIPS 2016 Tutorial: Generative Adversarial Networks](https://mlsanity.com/paper/1701.00160/) (2016)
- [FLASH: A Faster Optimizer for SBSE Tasks](https://mlsanity.com/paper/1705.05018/) (2017)

## Built on by (most cited first, 6 of 6)

- [Latent Imitator: Generating Natural Individual Discriminatory Instances for Black-Box Fairness Testing](https://mlsanity.com/paper/2305.11602/) (2023)
- [Wasserstein Generative Adversarial Networks for Online Test Generation for Cyber Physical Systems](https://mlsanity.com/paper/2205.11060/) (2022)
- [Requirement falsification for cyber-physical systems using generative models](https://mlsanity.com/paper/2310.20493/) (2023)
- [Falsification of Multiple Requirements for Cyber-Physical Systems Using Online Generative Adversarial Networks and Multi-Armed Bandits](https://mlsanity.com/paper/2205.11057/) (2022)
- [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)

## Abstract (arXiv)

In this paper we present a novel algorithm for automatic performance testing that uses an online variant of the Generative Adversarial Network (GAN) to optimize the test generation process. The objective of the proposed approach is to generate, for a given test budget, a test suite containing a high number of tests revealing performance defects. This is achieved using a GAN to generate the tests and predict their outcome. This GAN is trained online while generating and executing the tests. The proposed approach does not require a prior training set or model of the system under test. We provide an initial evaluation the algorithm using an example test system, and compare the obtained results with other possible approaches. We consider that the presented algorithm serves as a proof of concept and we hope that it can spark a research discussion on the application of GANs to test generation.