Online GANs for Automatic Performance Testing
This addresses performance testing in software engineering, but it is incremental as it adapts existing GAN methods to a new application area.
The paper tackles the problem of generating performance tests within a limited budget by introducing an online GAN algorithm that creates tests and predicts outcomes without prior training data, achieving an initial evaluation on an example system.
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.