Ajax-based Web 2.0 applications rely on stateful asynchronous client/server communication, and client-side runtime manipulation of the DOM tree. This not only makes them fundamentally different from traditional web applications, but also more error-prone and harder to test. We propose a method for testing Ajax applications automatically, based on a crawler to infer a state-flow graph for all (client-side) user interface states. We identify Ajax-specific faults that can occur in such states (related to, e.g., DOM validity, error messages, discoverability, back-button compatibility) as well as DOM-tree invariants that can serve as oracles to detect such faults. Our approach, called Atusa, is implemented in a tool offering generic invariant checking components, a plugin-mechanism to add application-specific state validators, and generation of a test suite covering the paths obtained during crawling. We describe three case studies, consisting of six subjects, evaluating the type of invariants that can be obtained for Ajax applications as well as the fault revealing capabilities, scalability, required manual effort, and level of automation of our testing approach.

There is a growing trend to move desktop applications towards the web using advances made in web technologies such as Ajax. One common way to provide assurance about the correctness of such complex and evolving systems is through regression testing. Regression testing classical web applications has already been a notoriously daunting task because of the dynamism in web interfaces. Ajax applications pose an even greater challenge since the test case fragility degree is higher due to extensive run-time manipulation of the DOM tree and asynchronous client/server interactions. In this paper, we propose a technique, in which we automatically generate test cases and apply pipelined oracle comparators along with generated DOM templates, to deal with dynamic non-deterministic behavior in Ajax user interfaces. Our approach, implemented in Crawljax, is open source and provides a set of generic oracle comparators, template generators, and visualizations of test failure output. We describe two case studies evaluating the effectiveness, scalability, and required manual effort of the approach.