应用商店挖掘和分析:应用商店的MSR

M. Harman, Yue Jia, Yuanyuan Zhang
{"title":"应用商店挖掘和分析:应用商店的MSR","authors":"M. Harman, Yue Jia, Yuanyuan Zhang","doi":"10.1109/MSR.2012.6224306","DOIUrl":null,"url":null,"abstract":"This paper introduces app store mining and analysis as a form of software repository mining. Unlike other software repositories traditionally used in MSR work, app stores usually do not provide source code. However, they do provide a wealth of other information in the form of pricing and customer reviews. Therefore, we use data mining to extract feature information, which we then combine with more readily available information to analyse apps' technical, customer and business aspects. We applied our approach to the 32,108 non-zero priced apps available in the Blackberry app store in September 2011. Our results show that there is a strong correlation between customer rating and the rank of app downloads, though perhaps surprisingly, there is no correlation between price and downloads, nor between price and rating. More importantly, we show that these correlation findings carry over to (and are even occasionally enhanced within) the space of data mined app features, providing evidence that our `App store MSR' approach can be valuable to app developers.","PeriodicalId":383774,"journal":{"name":"2012 9th IEEE Working Conference on Mining Software Repositories (MSR)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"348","resultStr":"{\"title\":\"App store mining and analysis: MSR for app stores\",\"authors\":\"M. Harman, Yue Jia, Yuanyuan Zhang\",\"doi\":\"10.1109/MSR.2012.6224306\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper introduces app store mining and analysis as a form of software repository mining. Unlike other software repositories traditionally used in MSR work, app stores usually do not provide source code. However, they do provide a wealth of other information in the form of pricing and customer reviews. Therefore, we use data mining to extract feature information, which we then combine with more readily available information to analyse apps' technical, customer and business aspects. We applied our approach to the 32,108 non-zero priced apps available in the Blackberry app store in September 2011. Our results show that there is a strong correlation between customer rating and the rank of app downloads, though perhaps surprisingly, there is no correlation between price and downloads, nor between price and rating. More importantly, we show that these correlation findings carry over to (and are even occasionally enhanced within) the space of data mined app features, providing evidence that our `App store MSR' approach can be valuable to app developers.\",\"PeriodicalId\":383774,\"journal\":{\"name\":\"2012 9th IEEE Working Conference on Mining Software Repositories (MSR)\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-06-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"348\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 9th IEEE Working Conference on Mining Software Repositories (MSR)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MSR.2012.6224306\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 9th IEEE Working Conference on Mining Software Repositories (MSR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MSR.2012.6224306","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 348

摘要

本文介绍了应用商店挖掘和分析作为软件存储库挖掘的一种形式。与MSR工作中传统使用的其他软件库不同,应用商店通常不提供源代码。然而,它们确实以定价和客户评论的形式提供了丰富的其他信息。因此,我们使用数据挖掘来提取特征信息,然后将其与更容易获得的信息结合起来,分析应用程序的技术、客户和业务方面。我们将此方法应用于2011年9月黑莓应用商店中32108款非零定价应用。我们的研究结果显示,用户评价和应用下载量之间存在很强的相关性,但令人惊讶的是,价格和下载量之间没有相关性,价格和评价之间也没有相关性。更重要的是,我们表明这些相关性发现延续到(甚至偶尔在数据挖掘应用功能的空间中得到增强),证明我们的“应用商店MSR”方法对应用开发者是有价值的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
App store mining and analysis: MSR for app stores
This paper introduces app store mining and analysis as a form of software repository mining. Unlike other software repositories traditionally used in MSR work, app stores usually do not provide source code. However, they do provide a wealth of other information in the form of pricing and customer reviews. Therefore, we use data mining to extract feature information, which we then combine with more readily available information to analyse apps' technical, customer and business aspects. We applied our approach to the 32,108 non-zero priced apps available in the Blackberry app store in September 2011. Our results show that there is a strong correlation between customer rating and the rank of app downloads, though perhaps surprisingly, there is no correlation between price and downloads, nor between price and rating. More importantly, we show that these correlation findings carry over to (and are even occasionally enhanced within) the space of data mined app features, providing evidence that our `App store MSR' approach can be valuable to app developers.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
MINCE: Mining change history of Android project Co-evolution of logical couplings and commits for defect estimation Analysis of customer satisfaction survey data Do faster releases improve software quality? An empirical case study of Mozilla Firefox Why do software packages conflict?
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1