A scalable approach to user-session based testing of Web applications through concept analysis

The continuous use of the Web for daily operations by businesses, consumers, and government has created a great demand for reliable Web applications. One promising approach to testing the functionality of Web applications leverages user-session data collected by Web servers. This approach automatically generates test cases based on real user profiles. The key contribution of This work is the application of concept analysis for clustering user sessions for test suite reduction. Existing incremental concept analysis algorithms can be exploited to avoid collecting large user-session data sets and thus provide scalability. We have completely automated the process from user session collection and reduction through replay. Our incremental test suite update algorithm coupled with our experimental study indicate that concept analysis provides a promising means for incrementally updating reduced test suites in response to newly captured user sessions with some loss in fault detection capability and practically no coverage loss.