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, Palomba et al. recently proposed CHANGEADVISOR that 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 Palomba et 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 novel weight 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 that Where2Change can successfully locate more source code classes related to the change requests for more user feedback clusters than CHANGEADVISOR as 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 of Where2Change and two previous Information Retrieval (IR)-based fault localization technologies: BLUiR and BLIA . 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.

中文翻译：

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Where2Change：应用评论的变更请求本地化

数以百万计的移动应用程序已投放市场。开发人员需要维护这些应用程序，以便他们能够继续使最终用户受益。开发人员通常从用户评论中提取有用的信息来维护和发展移动应用程序。开发人员在阅读用户评论时需要做的一项重要活动是找到与请求更改相关的源代码。不幸的是，这项手动工作既昂贵又耗时，因为：(1) 一个应用程序可以收到数千条评论，以及 (2) 一个移动应用程序可能包含数百个源代码文件。为了应对这一挑战，Palomba等人。最近提出变更顾问利用用户评论来定位要更改的源代码。但是，我们发现它无法识别要更改的部分评论的真实源代码。在这项工作中，我们的目标是推进 Palomba等人。 的工作提出了一种新方法，可以在变化定位中实现更高的准确性。我们的方法首先从用户评论中提取信息句子（即用户反馈），并识别与各种问题和功能请求相关的用户反馈，然后将相应的用户反馈分组。每个组报告相似的用户需求。接下来，这些组通过使用映射到问题报告$Word2Vec$ . 由此产生的由用户反馈及其相应问题报告组成的丰富文本用于识别应使用我们新颖的 基于权重选择的余弦相似度度量来更改的源代码类。我们已经评估了新提议的变更请求本地化方法（ Where2Change ) 对 10 个开源移动应用程序的 31,597 条用户评论和 3,272 份问题报告。实验表明Where2Change 可以成功地为更多的用户反馈集群定位与变更请求相关的更多源代码类 变更顾问正如更高的 Top-N 和 Recall 值所证明的那样。Top-1 的差异达到 17，Top-3 的差异达到 18.1，Top-5 的差异达到 17.9，Recall 的差异达到 50.08%。此外，我们还比较了Where2Change 以及之前两种基于信息检索 (IR) 的故障定位技术： 蓝光 和 BLIA . 结果表明，我们的方法比他们表现得更好。作为我们工作的重要组成部分，我们进行了一项实证研究，以调查使用用户评论和历史问题报告进行变更请求本地化的价值；结果表明，历史问题报告有助于提高变更本地化的性能。