A Recommender System of Buggy App Checkers for App Store Moderators

被引：27
作者：
Gomez, Maria [1 ]
Rouvoy, Romain [1 ]
Monperrus, Martin [1 ]
Seinturier, Lionel [1 ]
机构：
[1] Univ Lille, Inria, Villeneuve Dascq, France
来源：
2ND ACM INTERNATIONAL CONFERENCE ON MOBILE SOFTWARE ENGINEERING AND SYSTEMS MOBILESOFT 2015 | 2015年
关键词：
D O I：
10.1109/MobileSoft.2015.8
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
The popularity of smartphones is leading to an ever growing number of mobile apps that are published in official app stores. However, users might experience bugs and crashes for some of these apps. In this paper, we perform an empirical study of the official Google Play Store to automatically mine for such error-suspicious apps. We use the knowledge inferred from this analysis to build a recommender system of buggy app checkers. More specifically, we analyze the permissions and the user reviews of 46, 644 apps to identify potential correlations between error-sensitive permissions and error-related reviews along time. This study reveals error-sensitive permissions and patterns that potentially induce the errors reported online by users. As a result, our systems give app store moderators efficient static checkers to predict buggy apps before they harm the reputation of the app store as a whole.
引用
收藏
页码：1 / 11
页数：11
相关论文
共 29 条
[1]  
[Anonymous], 2011, W2SP 2011 CMU US
[2]  
[Anonymous], 2014, P 36 INT C SOFTW ENG
[3]  
[Anonymous], 2002, Mallet: a machine learning for languagetoolkit
[4]  
[Anonymous], USENIX SECURITY
[5]  
Au K. W. Y., 2012, ACM C COMP COMM SEC, P217, DOI [10.1145/2382196.2382222, DOI 10.1145/2382196.2382222]
[6]   A Methodology for Empirical Analysis of Permission-Based Security Models and its Application to Android [J].
Barrera, David ;
Kayacik, H. Guenes ;
van Oorschot, P. C. ;
Somayaji, Anil .
PROCEEDINGS OF THE 17TH ACM CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY (CCS'10), 2010, :73-84
[7]  
Bartel A, 2012, IEEE INT CONF AUTOM, P274, DOI 10.1145/2351676.2351722
[8]   Latent Dirichlet allocation [J].
Blei, DM ;
Ng, AY ;
Jordan, MI .
JOURNAL OF MACHINE LEARNING RESEARCH, 2003, 3 (4-5) :993-1022
[9]  
Chia PernHui., 2012, WWW 12 P 21 INT C WO, P311, DOI DOI 10.1145/2187836.2187879
[10]  
Clark P., 1991, MACHINE LEARNING EWS, P151, DOI 10.1007/bfb0017011
123