Automatic test case generation is a key ingredient of an efficient and cost-effective software verification process. In this paper we focus on testing applications that interact with the users through a GUI, and present Auto Black Test, a technique to automatically generate test cases at the system level. Auto Black Test uses reinforcement learning, in particular Q-Learning, to learn how to interact with the application under test and stimulate its functionalities. The empirical results show that Auto Black Test can execute a relevant portion of the code of the application under test, and can reveal previously unknown problems by working at the system level and interacting only through the GUI.
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机译：自动生成测试用例是有效且具有成本效益的软件验证过程的关键组成部分。在本文中，我们着重于测试通过GUI与用户交互的应用程序，并提出了Auto Black Test（一种自动在系统级别自动生成测试用例的技术）。 Auto Black Test使用强化学习（尤其是Q-Learning）来学习如何与被测应用程序进行交互并激发其功能。实验结果表明，自动黑测试可以执行被测应用程序代码的相关部分，并且可以通过在系统级别工作并仅通过GUI进行交互来揭示以前未知的问题。
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