Katie Baldwin's Research Page


Web applications must be dependable as the number and popularity of web applications increases, and people become more dependent on them. Web applications are difficult and expensive to test because of the large input space and frequent changes. Thus, their characteristics demand an efficient and effective way of automating the test case generation process. Current approaches to automatic test case generation for web applications do not attain all the goals of representing user behavior, maintaining good code coverage, and reducing the number of test cases. This research is based on Sant et al.'s user-session-based test case generation approach, which applies statistical language learning algorithms to create control and data models, where a control model represents the possible URL sequences and the data model represents the possible parameter values. Through analyzing user sessions, we identify factors that impact values in user sessions, and use these results to develop a set of data models for automatic test case generation.