Abstract
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 <i>et al.</i> recently proposed <monospace>CHANGEADVISOR</monospace> 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 <i>et al.</i> '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 <inline-formula><tex-math notation="LaTeX">$Word2Vec$</tex-math></inline-formula> . 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 <i>weight selection</i> -based cosine similarity metric. We have evaluated the new proposed change request localization approach ( <monospace>Where2Change</monospace> ) on 31,597 user reviews and 3,272 issue reports of 10 open source mobile apps. The experiments demonstrate that <monospace>Where2Change</monospace> can successfully locate more source code classes related to the change requests for more user feedback clusters than <monospace>CHANGEADVISOR</monospace> 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 <monospace>Where2Change</monospace> and two previous Information Retrieval (IR)-based fault localization technologies: <monospace>BLUiR</monospace> and <monospace>BLIA</monospace> . 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.
Highlights
IntroductionAs the number of mobile devices (e.g., smartphones and tablet computers) and their applications (apps) increases, the task of maintaining mobile apps is becoming more important [1]
As the number of mobile devices and their applications increases, the task of maintaining mobile apps is becoming more important [1]
In order to explain why we use user feedback clusters as queries rather than issue reports, we conduct the empirical study for user reviews and issue reports, the results shown that issue reports can help to improve the performance of change request localization but cannot replace user reviews to conduct this task for mobile apps
Summary
As the number of mobile devices (e.g., smartphones and tablet computers) and their applications (apps) increases, the task of maintaining mobile apps is becoming more important [1]. In online app stores such as Google Play Store, Apple Store, and Windows Phone App Store, users are allowed to evaluate each app by using scores (i.e., five stars) and posting their reviews These reviews are free-form text that may include important information such as bugs that need to be fixed for developers. We introduce information retrieval (IR) based change request localization and a sample issue report for a mobile app. Information retrieval (IR) based change request localization tech- 9 mViewPager . Proaches, each software artifact (e.g., issue report and user review) is treated as a query, and the source code (e.g., source files, classes, and methods) to be searched as the document collection. A list of 2.1.2 Issue reports ranked documents are returned
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