FuRong

Abstract

Automated testing has been widely used to ensure the quality of Android applications. However, incomprehensible testing results make it difficult for developers to understand and fix potential bugs. This paper proposes FuRong, a novel tool, to fuse bug reports of high-readability and strong-guiding-ability via analyzing the automated testing results on multi-devices. FuRong builds a bug model with complete context information, such as screenshots, operation sequences, and logs from multi-devices, and then leverages pretrained Decision Tree classifier (with 18 bug category labels) to classify bugs. FuRong deduplicates the classified bugs via Levenshtein distance and finally generates the easy-to-understand report, not only context information of bugs, where possible causes and fix suggestions for each bug category are also provided. An empirical study of 8 open-source Android applications with automated testing on 20 devices has been conducted, the results show the effectiveness of FuRong, which has a bug classification precision of 93.4% and a bug classification accuracy of 87.9%. Video URL: https://youtu.be/LUkFTc32B6k