Mobile apps have become an indispensable part of people's daily life. When people use mobile applications, they often comment on them. This comment information becomes a key data source for mobile application developers to maintain software. Then, many user reviews make it difficult for developers to capture important information in a short time, thus affecting users’ satisfaction. Recently, the master student Youshuai Tan from the School of Computer Science and Engineering of MUST and his advisor Associate Professor Tao Zhang proposed a new method STRE, which analyzes historical user review information and then recommends the time to developers when stopping reading user reviews. At this time point, the developer has obtained enough information to fix the important bugs raised by the users. This method can greatly save developers’ time to reading user reviews, thereby improving the efficiency of software maintenance. Through evaluation experiments on 62 recent versions of five popular mobile applications (Zoom, YouTube, Amazon Shopping, Twitter, and Starbucks), the tool saves developers time by up to 98.33%. This work has been strongly supported by the WeMust development team of MUST, whose leader Mr. Husheng Yuan believes that this tool can greatly help developers improve the efficiency of maintaining mobile applications and looks forward to the tool being commercialized as soon as possible. The WeMust team will provide follow-up platform and data support.
The above research achievements have been published in IEEE Transactions on Software Engineering, an international top-tier journal in the field of software engineering (impact factor is 9.322, ranking 3/110 in Computer Science, Software Engineering). Mr. Youshuai Tan, a master student at the School of Computer Science and Engineering, is the first author of this article, MUST is the first affiliation, and Associate Professor Dr. Tao Zhang is the only corresponding author of this article. This work was completed in collaboration with Associate Professor Dr. Weiyi Shang from Concordia University and Associate Professor Dr. Xiapu Luo from Hong Kong Polytechnic University, and was supported by the Macao Science and Technology Development Fund (0014/2022/A, 0047/2020/A1). It is worth mentioning that this achievement is the second time that Associate Professor Tao Zhang’s team has published a paper in this journal. The first work on using user comment information to locate user change requests is also the first important scientific research achievement published in this journal in Macao.
Master Student Mr. Youshuai Tan
Associate Professor Tao Zhang
IEEE Transactions on Software Engineering (TSE) is one of the two most influential top-tier journals in the field of software engineering, mainly reporting the latest and most important scientific research results and innovative technologies in the field of software engineering. The journal has a high international reputation. The journal is also a Rank A journal recommended by the China Computer Federation (the highest level, the China Computer Federation defines this category as one of the few top journals and conferences in the world, and Chinese scholars are encouraged to make breakthroughs).
Youshuai Tan, Jinfu Chen, Weiyi Shang, Tao Zhang*, Sen Fang, Xiapu Luo, Zijie Chen, and Shuhao Qi, “STRE: An Automated Approach to Suggesting App Developers When to Stop Reading Reviews”, IEEE Transactions on Software Engineering (TSE), Early Access, 2023.
Article publication link: https://ieeexplore.ieee.org/document/10149402