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Crowdsourced testing is an emerging trend in software testing, which relies on crowd workers to accomplish test tasks. Due to the cost constraint, a test task usually involves a limited number of crowd workers. Furthermore, more workers does not necessarily result in detecting more bugs. Different workers, who may have different testing experience and expertise, may make much differences in the test...
In crowdsourced testing, it is beneficial to automatically classify the test reports that actually reveal a fault – a true fault, from the large number of test reports submitted by crowd workers. Most of the existing approaches toward this task simply leverage historical data to train a machine learning classifier and classify the new incoming reports. However, our observation on real industrial data...
Mobile app testing is challenging since each test needs to be executed in a variety of operating contexts including heterogeneous devices, various wireless networks, and different locations. Crowdsourcing enables a mobile app testing to be distributed as a crowdsourced task to leverage the crowd in a community. However, a high test quality and expected test context coverage are difficult to achieve...
In crowdsourced testing, an important task is to identify the test reports that actually reveal fault — true fault, from the large number of test reports submitted by crowd workers. Most existing approaches towards this problem utilized supervised machine learning techniques, which often require users to manually label a large amount of training data. Such process is time-consuming and labor-intensive...
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