Listening to the Crowd for the Change File Localization of Mobile Apps
Jianmao Xiao, Jia Zeng, Su Fen Yao, Yuanlong Cao, Yiwen Jiang, Wenle Wang · 2021
In order to allow developers to implement operable code-level modification tasks based on user feedback directly, so as to achieve rapid and continuous app updates and releases. We propose an efficient automated approach named LCFCR, which leverages natural language processing and clustering algorithms to group user reviews. Then, it enriches the semantic information of each group. Further, by combining the textual information from both commit messages and source code, it automatically localizes potential change files. We have evaluated the LCFCR on 10 open source mobile apps. The experiments demonstrate that the proposed approach outperforms the state-of-the-art baseline work in terms of clustering and localization accuracy, and thus produces more reliable results.