Online GANs for Automatic Performance Testing

TL;DR

This paper introduces an online GAN-based algorithm for automatic performance testing that generates and predicts test outcomes in real-time without prior training, aiming to efficiently identify performance defects.

Contribution

It presents a novel online GAN approach for test generation that operates without pre-existing data, enabling adaptive and efficient performance testing.

Findings

Successfully generates high-quality test suites

Outperforms some baseline approaches in defect detection

Operates without prior training data

Abstract

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.