Model-based testing is a new and evolving technique for generating a suite of test cases from requirements. Testers using this approach concentrate on a data model and generation infrastructure instead of hand-crafting individual tests. Several relatively small studies have demonstrated how combinatorial test generation techniques allow testers to achieve broad coverage of the input domain with a small number of tests. We have conducted several relatively large projects in which we applied these techniques to systems with millions of lines of code. Given the complexity of testing, the modelbased testing approach was used in conjunction with test automation harnesses. Since no large empirical study has been conducted to measure efficacy of this new approach, we report on our experience with developing tools and methods in support of model-based testing. The four case studies presented here offer details and results of applying combinatorial test-generation techniques on a large scale to diverse applications. Based on the four projects, we offer our insights into what works in practice and our thoughts about obstacles to transferring this technology into testing organizations.
The huge amount of information available in the currently evolving world wide information infrastructure at any one time can easily overwhelm end-users. One way to address the information explosion is to use an "information filtering agent" which can select information according to the interest and/or need of an end-user. However, at present few such information filtering agents exist. In this study, we evaluate the use of feature-based approaches to user modcling with the purpose of creating a filtering agent for the video-on-demand application. We evaluate several feature and clique-based models for 10 voluntary subjects who provided ratings for the movies. Our preliminary results suggest that feature-based selection can be a useful tool to recommend movies according to the taste of the user and can be as effective as a movie rating expert. We compare our feature-based approach with a clique-based approach, which has advantages whcrc information from other users is available.
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