Online GANs for Automatic Performance Testing

Research output: Chapter in Book/Conference proceedingConference contributionScientificpeer-review


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.
Original languageEnglish
Title of host publication2021 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW)
ISBN (Print)978-1-6654-4457-6
Publication statusPublished - 2021
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Software Testing Verification and Validation Workshop -
Duration: 12 Apr 202116 Apr 2021


ConferenceIEEE International Conference on Software Testing Verification and Validation Workshop
Abbreviated title ICSTW


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