A Recommender System of Buggy App Checkers for App Store Moderators

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.

[1]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[2]  Rachel Harrison,et al.  Retrieving and analyzing mobile apps feature requests from online reviews , 2013, 2013 10th Working Conference on Mining Software Repositories (MSR).

[3]  Paul C. van Oorschot,et al.  A methodology for empirical analysis of permission-based security models and its application to android , 2010, CCS '10.

[4]  Jim Webber,et al.  Graph Databases: New Opportunities for Connected Data , 2013 .

[5]  Tao Xie,et al.  WHYPER: Towards Automating Risk Assessment of Mobile Applications , 2013, USENIX Security Symposium.

[6]  Wei Xu,et al.  Permlyzer: Analyzing permission usage in Android applications , 2013, 2013 IEEE 24th International Symposium on Software Reliability Engineering (ISSRE).

[7]  Fred J. Damerau,et al.  A technique for computer detection and correction of spelling errors , 1964, CACM.

[8]  Yves Le Traon,et al.  Automatically securing permission-based software by reducing the attack surface: an application to Android , 2012, 2012 Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering.

[9]  Steve Hanna,et al.  Android permissions demystified , 2011, CCS '11.

[10]  Christos Faloutsos,et al.  Why people hate your app: making sense of user feedback in a mobile app store , 2013, KDD.

[11]  David A. Wagner,et al.  Do Android users write about electric sheep? Examining consumer reviews in Google Play , 2013, 2013 IEEE 10th Consumer Communications and Networking Conference (CCNC).

[12]  Dawn Xiaodong Song,et al.  Mining Permission Request Patterns from Android and Facebook Applications , 2012, 2012 IEEE 12th International Conference on Data Mining.

[13]  Alessandra Gorla,et al.  Checking app behavior against app descriptions , 2014, ICSE.

[14]  Premkumar T. Devanbu,et al.  Asking for (and about) permissions used by Android apps , 2013, 2013 10th Working Conference on Mining Software Repositories (MSR).

[15]  Gabriele Bavota,et al.  API change and fault proneness: a threat to the success of Android apps , 2013, ESEC/FSE 2013.

[16]  A. Strauss,et al.  Grounded theory methodology: An overview. , 1994 .

[17]  Zhen Huang,et al.  PScout: analyzing the Android permission specification , 2012, CCS.

[18]  Ning Chen,et al.  AR-miner: mining informative reviews for developers from mobile app marketplace , 2014, ICSE.

[19]  Pern Hui Chia,et al.  Is this app safe?: a large scale study on application permissions and risk signals , 2012, WWW.

[20]  Todd D. Millstein,et al.  Dr. Android and Mr. Hide: fine-grained permissions in android applications , 2012, SPSM '12.

[21]  Peter Clark,et al.  Rule Induction with CN2: Some Recent Improvements , 1991, EWSL.

[22]  金田 重郎,et al.  C4.5: Programs for Machine Learning (書評) , 1995 .

[23]  Romain Rouvoy,et al.  Dynamic Deployment of Sensing Experiments in the Wild Using Smartphones , 2013, DAIS.

[24]  Michalis Faloutsos,et al.  Permission evolution in the Android ecosystem , 2012, ACSAC '12.