Prioritizing the devices to test your app on: a case study of Android game apps

Hammad Khalid, M. Nagappan, Emad Shihab, A. Hassan
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引用次数: 90

Abstract

Star ratings that are given by the users of mobile apps directly impact the revenue of its developers. At the same time, for popular platforms like Android, these apps must run on hundreds of devices increasing the chance for device-specific problems. Device-specific problems could impact the rating assigned to an app, given the varying capabilities of devices (e.g., hardware and software). To fix device-specific problems developers must test their apps on a large number of Android devices, which is costly and inefficient. Therefore, to help developers pick which devices to test their apps on, we propose using the devices that are mentioned in user reviews. We mine the user reviews of 99 free game apps and find that, apps receive user reviews from a large number of devices: between 38 to 132 unique devices. However, most of the reviews (80%) originate from a small subset of devices (on average, 33%). Furthermore, we find that developers of new game apps with no reviews can use the review data of similar game apps to select the devices that they should focus on first. Finally, among the set of devices that generate the most reviews for an app, we find that some devices tend to generate worse ratings than others. Our findings indicate that focusing on the devices with the most reviews (in particular the ones with negative ratings), developers can effectively prioritize their limited Quality Assurance (QA) efforts, since these devices have the greatest impact on ratings.
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优先选择测试应用的设备:Android游戏应用的案例研究
手机应用用户给出的星级评价直接影响着开发者的收益。与此同时,对于像Android这样的流行平台,这些应用必须在数百种设备上运行,这增加了出现特定设备问题的可能性。考虑到设备的不同功能(如硬件和软件),特定于设备的问题可能会影响分配给应用程序的评级。为了解决特定设备的问题,开发者必须在大量Android设备上测试他们的应用,这既昂贵又低效。因此,为了帮助开发者选择测试应用的设备,我们建议使用用户评论中提到的设备。我们对99款免费游戏应用的用户评论进行了分析,发现应用收到的用户评论来自38至132个不同设备。然而,大多数评论(80%)来自一小部分设备(平均33%)。此外,我们发现没有评论的新游戏应用的开发者可以使用类似游戏应用的评论数据来选择他们应该首先关注的设备。最后,在为应用产生最多评论的设备中,我们发现有些设备的评分往往比其他设备低。我们的研究结果表明,专注于评论最多的设备(特别是负面评价),开发者可以有效地优先考虑他们有限的质量保证(QA)工作,因为这些设备对评级的影响最大。
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