An exploration of statistical models for automated test case generation

In this paper, we develop methods that use logged user data to build models of a web application. Logged user data captures dynamic behavior of an application that can be useful for addressing the challenging problems of testing web applications. Our approach automatically builds statistical models of user sessions and automatically derives test cases from these models. We provide several alternative modeling approaches based on statistical machine learning methods. We investigate the effectiveness of the test suites generated from our methods by performing a preliminary study that evaluates the generated test cases. The results of this study demonstrate that our techniques are able to generate test cases that achieve high coverage and accurately model user behavior. This study provides insights into improving our methods and motivates a larger study with a more diverse set of applications and testing metrics.