Performance exploration of the REST API using online GAN
Léonard, Cédric (2022)
Léonard, Cédric
2022
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2022061446357
https://urn.fi/URN:NBN:fi-fe2022061446357
Tiivistelmä
Performance Testing is a critically important step in the application development process. Manually achieving performance exploration comes with high human-cost and, in general, inefficiency. Software testing automation is a research area with many contributions which observes frequent advancements. In the context of the SBST 2022 CPS tool competition, Åbo Akademi University laboratory has published a new Machine Learning technique that merges a GAN-based model and the online learning process: Online Generative Adversarial Network (OGAN).
This thesis approached the performance exploration problem using the OGAN algorithm on particular applications: REST APIs. This thesis sets the required context and develops the information background concerning: REST APIs principles, the Performance Testing process and the usage of Deep Neural Network, specifically Generative Adversarial Networks (GANs), in an online learning approach. The Machine Learning technique tackled: OGAN is presented and its algorithm detailed and discussed. Finally, an application of this technique is proposed and explored on the PetClinic REST API.
This thesis approached the performance exploration problem using the OGAN algorithm on particular applications: REST APIs. This thesis sets the required context and develops the information background concerning: REST APIs principles, the Performance Testing process and the usage of Deep Neural Network, specifically Generative Adversarial Networks (GANs), in an online learning approach. The Machine Learning technique tackled: OGAN is presented and its algorithm detailed and discussed. Finally, an application of this technique is proposed and explored on the PetClinic REST API.