In this paper, we discuss a model based software testing method for testing of the web applications using StateCharts. To this end, we briefly describe the test coverage technique, editorial tools for drawing StateCharts, modeling concurrency in web applications, and test metrics. A simple web application has been used to show the feasibility of our approach.
Though subgraph matching has been extensively studied as a query paradigm in semantic web and social network data environments, a user can get a large number of answers in response to a query. Just like Google does, these answers can be shown to the user in accordance with an importance ranking. In this paper, we present scalable algorithms to find the top-K answers to a practically important subset of SPARQLqueries, denoted as importance queries, via a suite of pruning techniques. We test our algorithms on multiple real-world graph data sets, showing that our algorithms are efficient even on networks with up to 6M vertices and 15M edges and far more efficient than popular triple stores.
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