Using Knowledge Transfer and Rough Set to Predict the Severity of Android Test Reports via Text Mining

Crowdsourcing is an appealing and economic solution to software application testing because of its ability to reach a large international audience. Meanwhile, crowdsourced testing could have brought a lot of bug reports. Thus, in crowdsourced software testing, the inspection of a large number of test reports is an enormous but essential software maintenance task. Therefore, automatic prediction of the severity of crowdsourced test reports is important because of their high numbers and large proportion of noise. Most existing approaches to this problem utilize supervised machine learning techniques, which often require users to manually label a large number of training data. However, Android test reports are not labeled with their severity level, and manual labeling is time-consuming and labor-intensive. To address the above problems, we propose a Knowledge Transfer Classification (KTC) approach based on text mining and machine learning methods to predict the severity of test reports. Our approach obtains training data from bug repositories and uses knowledge transfer to predict the severity of Android test reports. In addition, our approach uses an Importance Degree Reduction (IDR) strategy based on rough set to extract characteristic keywords to obtain more accurate reduction results. The results of several experiments indicate that our approach is beneficial for predicting the severity of android test reports.