Clustering on the Stream of Crowdsourced Testing

In this paper, we propose a clustering framework to analyze the log files generated along crowdsourcing mobile application testing. Our object is to automatically identify the type of testing work that the worker is performing as to reduce the work of developers clustering the test reports. By taking full data information of the log files, we establish the hierarchy of the testing data. Through the application of data processing and stream clustering methods, we accomplish the static mining and dynamic division of the test stream data. Experiments on a crowdsourcing mobile application testing dataset the efficacy of our approach.