Investigating Mobile Applications Quality in Official and Third-party Marketplaces

One of the winning factors of Android was the use of the Java programming language and the XML language for application development. Furthermore, the open-source license and the availability of reverse engineering tools stimulated the proliferation of third-party markets where users can download for free repackaged version of commercial app, facilitating the phenomenon of plagiarism. In this paper we present an empirical study aimed to define whether there are differences from the quality point of view in Android applications available in the official market and in third-party ones, investigating whether supervised and unsupervised models built with a set of features belonging to four categories (i.e., dimensional, complexity, object oriented and Android) are effective in app store detection.

[1]  Chris F. Kemerer,et al.  A Metrics Suite for Object Oriented Design , 2015, IEEE Trans. Software Eng..

[2]  Geoffrey Hecht,et al.  An Approach to Detect Android Antipatterns , 2015, 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering.

[3]  H. Charles Romesburg,et al.  Cluster analysis for researchers , 1984 .

[4]  Atanas Rountev,et al.  Testing for poor responsiveness in android applications , 2013, 2013 1st International Workshop on the Engineering of Mobile-Enabled Systems (MOBS).

[5]  Gerardo Canfora,et al.  Mobile malware detection using op-code frequency histograms , 2015, 2015 12th International Joint Conference on e-Business and Telecommunications (ICETE).

[6]  Brij B. Gupta,et al.  Android Applications Repackaging Detection Techniques for Smartphone Devices , 2016 .

[7]  Marjan Hericko,et al.  Using Object Oriented Software Metrics for Mobile Application Development , 2013, SQAMIA.

[8]  Robert V. Binder,et al.  Design for testability in object-oriented systems , 1994, CACM.

[9]  Ying Zou,et al.  An Exploratory Study on the Relation between User Interface Complexity and the Perceived Quality , 2014, ICWE.

[10]  David Lo,et al.  What are the characteristics of high-rated apps? A case study on free Android Applications , 2015, 2015 IEEE International Conference on Software Maintenance and Evolution (ICSME).

[11]  Anas N. Al-Rabadi,et al.  A comparison of modified reconstructability analysis and Ashenhurst‐Curtis decomposition of Boolean functions , 2004 .

[12]  Fabio Martinelli,et al.  R-PackDroid: API package-based characterization and detection of mobile ransomware , 2017, SAC.

[13]  Riccardo Scandariato,et al.  Predicting vulnerable classes in an Android application , 2012, MetriSec '12.

[14]  Aniello Cimitile,et al.  Machine Learning Meets iOS Malware: Identifying Malicious Applications on Apple Environment , 2017, ICISSP.

[15]  Aniello Cimitile,et al.  An exploratory study on the evolution of Android malware quality , 2018, J. Softw. Evol. Process..

[16]  Norman E. Fenton,et al.  Software metrics: roadmap , 2000, ICSE '00.

[17]  Gerardo Canfora,et al.  Evaluating Op-Code Frequency Histograms in Malware and Third-Party Mobile Applications , 2015, ICETE.

[18]  Linda H. Rosenberg,et al.  Software Quality Metrics for Object-Oriented Environments , 2002 .

[19]  Antonella Santone,et al.  Incremental construction of systems: An efficient characterization of the lacking sub-system , 2013, Sci. Comput. Program..

[20]  Antonella Santone,et al.  Hey Malware, I Can Find You! , 2016, 2016 IEEE 25th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE).

[21]  Romain Rouvoy,et al.  Detecting Antipatterns in Android Apps , 2015, 2015 2nd ACM International Conference on Mobile Software Engineering and Systems.

[22]  Gerardo Canfora,et al.  Composition-Malware: Building Android Malware at Run Time , 2015, 2015 10th International Conference on Availability, Reliability and Security.

[23]  Antonella Santone,et al.  Infer Gene Regulatory Networks from Time Series Data with Probabilistic Model Checking , 2015, 2015 IEEE/ACM 3rd FME Workshop on Formal Methods in Software Engineering.

[24]  Diomidis Spinellis,et al.  Undocumented and unchecked: exceptions that spell trouble , 2014, MSR 2014.

[25]  Antonella Santone,et al.  Car hacking identification through fuzzy logic algorithms , 2017, 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[26]  Rich Caruana,et al.  An empirical comparison of supervised learning algorithms , 2006, ICML.

[27]  Gerardo Canfora,et al.  Metamorphic Malware Detection Using Code Metrics , 2014, Inf. Secur. J. A Glob. Perspect..

[28]  Antonella Santone,et al.  De novo reconstruction of gene regulatory networks from time series data, an approach based on formal methods. , 2014, Methods.

[29]  Gerardo Canfora,et al.  LEILA: Formal Tool for Identifying Mobile Malicious Behaviour , 2019, IEEE Transactions on Software Engineering.