A dataset of Mobile application reviews for classifying reviews into software Engineering's maintenance tasks using data mining techniques

This dataset has been collected from two different sources. The first dataset was taken from [1] and collected by Panichella et al. We obtained this dataset from Dr. Sebastiano Panichella via email. This dataset contains reviews of the AngryBirds, Dropbox, and Evernote app, which were taken from Apple’s App Store, other reviews were taken from Android’s Google Play store such as TripAdvisor, PicsArt, Pinterest and Whatsapp. This dataset consist of with 1390 reviews from all previously mentioned apps and all reviews were classified into four classes related to Software engineering’s maintenance task as follows: 192 reviews as Feature Request (FR), 494 reviews as Problem Discovery (PD), 603 reviews as Information Gaing (IG) and 101 reviews as Information Seeking (IS). We indicate to this dataset as “Pan Dataset”. The second dataset is used in [2] and prepared by Maalej et al. It is available at Hamburg University website on this direct link (https://mast.informatik.uni-hamburg.de/app-review-analysis). The truth dataset contains 3691 reviews from different Google’s apps store and Apple’s app store. We indicate to this dataset as “maalej dataset”. All reviews were classified into four classes related to Software engineering’s maintenance task as follows: 252 reviews as Feature Request (FR), 370 reviews as bug report/Problem Discovery (BR/PD), 607 reviews as User Experience (UE) and 2461 reviews as Rating (RT)本数据集源自两个不同来源。第一个数据集取自文献[1]，由Panichella等人采集，我们通过电子邮件从Sebastiano Panichella博士处获取了该数据集。该数据集包含来自苹果应用商店（Apple’s App Store）的《愤怒的小鸟》（AngryBirds）、Dropbox及印象笔记（Evernote）的应用评论，其余评论则取自安卓谷歌应用商店的TripAdvisor、PicsArt、Pinterest及WhatsApp应用。该数据集共包含上述所有应用的1390条评论，所有评论均被划分为与软件工程维护任务相关的四个类别：192条特征请求（Feature Request，FR）、494条问题发现（Problem Discovery，PD）、603条信息获取（Information Gaining，原文笔误为Gaing）以及101条信息搜索（Information Seeking，IS）。我们将该数据集命名为“Pan数据集”。第二个数据集取自文献[2]，由Maalej等人整理，可通过汉堡大学官网的直接链接（https://mast.informatik.uni-hamburg.de/app-review-analysis）获取。该真值数据集包含来自谷歌应用商店与苹果应用商店的3691条不同应用的评论，我们将其命名为“Maalej数据集”。所有评论均被划分为与软件工程维护任务相关的四个类别：252条特征请求（FR）、370条缺陷报告/问题发现（Bug Report/Problem Discovery，BR/PD）、607条用户体验（User Experience，UE）以及2461条评分（Rating，RT）。