Paper Info
Title
Automated Test Input Generation for Android: Towards Getting There in an Industrial Case.
Abstract
Monkey, a random testing tool from Google, has been popularly used in industrial practices for automatic test input generation for Android due to its applicability to a variety of application settings, e.g., ease of use and compatibility with different Android platforms. Recently, Monkey has been under the spotlight of the research community: recent studies found out that none of the studied tools from the academia were actually better than Monkey when applied on a set of open source Android apps. Our recent efforts performed the first case study of applying Monkey on WeChat, a popular messenger app with over 800 million monthly active users, and revealed many limitations of Monkey along with developing our improved approach to alleviate some of these limitations. In this paper, we explore two optimization techniques to improve the effectiveness and efficiency of our previous approach. We also conduct manual categorization of not-covered activities and two automatic coverage-analysis techniques to provide insightful information about the not-covered code entities. Lastly, we present findings of our empirical studies of conducting automatic random testing on WeChat with the preceding techniques.
Year
DOI
Venue
2017
10.1109/ICSE-SEIP.2017.32
ICSE-SEIP
Keywords
Field
DocType
automated test input generation,random testing tool,Google,Monkey,open source Android apps,WeChat,optimization techniques
Data science,Categorization,Random testing,Android (operating system),Systems engineering,Software engineering,Computer science,Usability,Atmospheric measurements,Java,Empirical research,Humanoid robot
Conference
ISBN
Citations 
PageRank 
978-1-5386-2718-1
8
0.47
References 
Authors
14
9
Name
Order
Citations
PageRank
Haibing Zheng1161.04
Dengfeng Li2392.69
Beihai Liang380.47
Xia Zeng4321.84
Wujie Zheng525415.92
Yuetang Deng6594.81
Wing Lam71728.81
Wei Yang843720.57
Tao Xie95978304.97