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A great number of mobile applications (apps) have been released to the market. Therefore, software maintenance for these apps become an important and challenging task. For each app, developers usually submit issue reports to report the bugs, the features, the questions, and other changes appearing in it. In the process of software maintenance, developers refer to the corresponding labels to decide which one should be fixed first. If the label of an issue report is “bug” which means the report describes a serious error, developers should fix the bug first. Otherwise, if the label is not “bug” (e.g., feature or question), developers can resolve it later. However, according to our investigation, 36.7% of issue reports in top-10 popular mobile apps are not labeled. In other words, there are not any labels in them. It is difficult for developers to decide which issue should be resolved preferentially. To resolve this problem, we propose a method to verify whether an issue report describes a bug or not by using user reviews. Developers usually extract useful information from user reviews to maintain mobile apps. In this work, we utilize tf • idf, Word2Vec, and Microsoft Concept Graph (MCG) to compute the textual similarity between issue reports and user reviews related to real bug in order to find the issue reports which describe the real bugs. As a result, our approach with Word2Vec performs the best among three similarity metrics. Keywords-issue report; user reviews; simialrity metrics; mobile apps; software maintenance

Bug or Not Bug? Labeling Issue Reports via User Reviews for Mobile Apps (S)