Applying Concept Analysis to User-Session-Based Testing of Web Applications

The continuous use of the Web for daily operations by businesses, consumers, and the government has created a great demand for reliable Web applications. One promising approach to testing the functionality of Web applications leverages the user-session data collected by Web servers. User-session-based testing automatically generates test cases based on real user profiles. The key contribution of this paper is the application of concept analysis for clustering user sessions and a set of heuristics for test case selection. Existing incremental concept analysis algorithms are exploited to avoid collecting and maintaining large user-session data sets and to thus provide scalability. We have completely automated the process from user session collection and test suite reduction through test case replay. Our incremental test suite update algorithm, coupled with our experimental study, indicates that concept analysis provides a promising means for incrementally updating reduced test suites in response to newly captured user sessions with little loss in fault detection capability and program coverage.