Where2Change: Change Request Localization for App Reviews
Tao Zhang, Jiachi Chen, Xian Zhan, Xiapu Luo, David Lo, He Jiang · IEEE Transactions on Software Engineering · 2019
Million of mobile apps have been released to the market. Developers need to maintain these apps so that they can continue to benefit end users. Developers usually extract useful information from user reviews to maintain and evolve mobile apps. One of the important activities that developers need to do while reading user reviews is to locate the source code related to requested changes. Unfortunately, this manual work is costly and time consuming since: (1) an app can receive thousands of reviews, and (2) a mobile app can consist of hundreds of source code files. To address this challenge, Palombaet al.recently proposedCHANGEADVISORthat utilizes user reviews to locate source code to be changed. However, we find that it cannot identify real source code to be changed for part of reviews. In this work, we aim to advance Palombaet al.'s work by proposing a novel approach that can achieve higher accuracy in change localization. Our approach first extracts the informative sentences (i.e., user feedback) from user reviews and identifies user feedback related to various problems and feature requests, and then cluster the corresponding user feedback into groups. Each group reports the similar users’ needs. Next, these groups are mapped to issue reports by using$Word2Vec$. The resultant enriched text consisting of user feedback and their corresponding issue reports is used to identify source code classes that should be changed by using our novelweight selection-based cosine similarity metric. We have evaluated the new proposed change request localization approach (Where2Change) on 31,597 user reviews and 3,272 issue reports of 10 open source mobile apps. The experiments demonstrate thatWhere2Changecan successfully locate more source code classes related to the change requests for more user feedback clusters thanCHANGEADVISORas demonstrated by higher Top-N and Recall values. The differences reach up to 17 for Top-1, 18.1 for Top-3, 17.9 for Top-5, and 50.08 percent for Recall. In addition, we also compare the performance ofWhere2Changeand two previous Information Retrieval (IR)-based fault localization technologies:BLUiRandBLIA. The results showed that our approach performs better than them. As an important part of our work, we conduct an empirical study to investigate the value of using both user reviews and historical issue reports for change request localization; the results shown that historical issue reports can help to improve the performance of change localization.