Mobile crowdsourced test report prioritization based on text and image understanding

Abstract

AbstractIn the process of mobile crowdsourced testing, a large number of test reports are generated, which often consist of short text and rich image information. One of the critical issues is how to review these reports efficiently. Researchers have recently proposed clustering, classification, and prioritization techniques to solve this problem. However, existing studies directly use related technologies to text description and segment the text content without further understanding of the text content. By deeply digging into the text information and distinguishing it according to its actual meaning, the sentences described in the text can be categorized into two types: Describing abnormal system behavior and describing reproduction steps. This paper proposes a mobile crowdsourced test report prioritization technique to improve performance. First, we use a TextCNN trained on large‐scale projects to distinguish the text descriptions of reports, then extract features from the text and screenshot information, respectively. Then we apply a clustering technique to gather similar reports. Finally, the inspection order is sampled from the clustering results. To validate our approach, we conduct experiments on six industrial crowdsourced projects. The results show that our method can detect all bugs faster in a limited time than existing prioritization methods, which can improve the bug reports review efficiency.