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.

中文翻译：

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用于自动性能测试的在线GAN

在本文中，我们提出了一种用于自动性能测试的新颖算法，该算法使用Generative Adversarial Network（GAN）的在线变体来优化测试生成过程。提出的方法的目的是针对给定的测试预算，生成包含大量显示性能缺陷的测试的测试套件。这是通过使用GAN生成测试并预测其结果来实现的。在生成和执行测试时，会对该GAN进行在线培训。所提出的方法不需要被测系统的事先训练集或模型。我们使用示例测试系统对算法进行了初步评估，并将获得的结果与其他可能的方法进行了比较。