Most software developed in recent years has a graphical user interface (GUI). The only way for the end-user to interact with the software application is through the GUI. Hence, acceptance and system testing of the software requires GUI testing. This paper presents a new technique for testing of GUI applications. Information on the actual usage of the application, in the form of "usage profiles," is used to ensure that a new version of the application will function correctly. Usage profiles, sequences of events that end-users execute on a GUI, are used to develop a probabilistic usage model of the application. An algorithm uses the model to generate test cases that represent events the user is most likely to execute. Reverse engineering methods are used to extract the underlying structure of the application. An empirical study on four open source GUI applications reveals that test suites generated from the probabilistic model are 0.2-22% of the size of test suites produced directly from usage profiles. Furthermore, the test suites generated from the model detect more faults per test case than those detected directly from the usage profiles, and detect faults not detected by the original profiles.

Abstract

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Most of today's event driven software (EDS) systems are tested using test cases that are carefully constructed as sequences of events; they test the execution of an event in the context of its preceding events. Because sizes of these test suites can be extremely large, researchers have developed techniques, such as reduction and minimization, to obtain test suites that are "similar" to the original test suite, but smaller. Existing similarity metrics mostly use code coverage; they do not consider the contextual relationships between events. Consequently, reduction based on such metrics may eliminate desirable test cases. In this paper, we present a new parameterized metric, CON T eSSi(n) which uses the context of n preceding events in test cases to develop a new context-aware notion of test suite similarity for EDS. This metric is defined and evaluated by comparing four test suites for each of four open source applications. Our results show that CON T eSSi(n) is a better indicator of the similarity of EDS test suites than existing metrics.

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