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 paper 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 usersession 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.
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.
This paper describes a program representation and algorithms for realizing a novel structural testing methodology that not only focuses on addressing the complex features of object-oriented languages, but also incorporates the structure of object-oriented software into the approach. The testing methodology is based on the construction of contextual defuse associations, which provide context to each definition and use of an object. Testing based on contextual defuse associations can provide increased test coverage by identifying multiple unique contextual defuse associations for the same context-free association. Such a testing methodology promotes more thorough and focused testing of the manipulation of objects in object-oriented programs. This paper presents a technique for the construction of contextual defuse associations, as well as detailed examples illustrating their construction, an analysis of the cost of constructing contextual defuse associations with this approach, and a description of a prototype testing tool that shows how the theoretical contributions of this work can be useful for structural test coverage.
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