Prioritisation of mobile crowdsourced test reports based on text analytics

Crowdsourced testing is a useful way to quickly identify defects and save time. However, the quality of crowdsourced testing reports varies widely since most testers are non-professionals. Effectively reviewing these test reports has become an urgent issue. To address this issue, researchers have proposed various techniques for classifying, clustering, and prioritising reports. These methods often directly extract text-related features without further understanding the textual content. By delving deeper into the textual information of crowdsourced testing reports, sentences within the text descriptions can be categorised into two types: defect descriptions and step descriptions. This paper presents a prioritisation method (TPLSI) for crowdsourced testing reports to enhance the efficiency of report review. Initially, a pre-trained LSTM is employed to classify the textual part of the crowdsourced testing reports. Subsequently, extract textual and visual features from the reports, merge these features, and use clustering techniques to group similar reports. Finally, the inspection sequence of the testing reports is determined by considering the severity of the issues reported in the crowdsourced testing reports. Furthermore, the experiments in this study were conducted on industrial crowdsourced projects from six different domains. The results indicate that the proposed method is capable of detecting errors more quickly within a limited timeframe.