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• W2968774823 abstract "Existing web test generators derive test paths from a navigational model of the web application, completed with either manually or randomly generated input values. However, manual test data selection is costly, while random generation often results in infeasible input sequences, which are rejected by the application under test. Random and search-based generation can achieve the desired level of model coverage only after a large number of test execution at- tempts, each slowed down by the need to interact with the browser during test execution. In this work, we present a novel web test generation algorithm that pre-selects the most promising candidate test cases based on their diversity from previously generated tests. As such, only the test cases that explore diverse behaviours of the application are considered for in-browser execution. We have implemented our approach in a tool called DIG. Our empirical evaluation on six real-world web applications shows that DIG achieves higher coverage and fault detection rates significantly earlier than crawling-based and search-based web test generators." @default.
• W2968774823 created "2019-08-22" @default.
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• W2968774823 date "2019-08-12" @default.
• W2968774823 modified "2023-10-16" @default.
• W2968774823 title "Diversity-based web test generation" @default.
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• W2968774823 doi "https://doi.org/10.1145/3338906.3338970" @default.
• W2968774823 hasPublicationYear "2019" @default.
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