• International Journal of Computer Science & Network Security
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• v.21 no.5
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• pp.217-221
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• 2021

Recent studies have indicated that mobile markets harbor applications (apps) that are either malicious or vulnerable, compromising millions of devices. Some studies indicate that 96% of companies' employees have used at least one malicious app. Some app stores do not employ security quality attributes regarding authorization, which is the function of specifying access rights to access control resources. However, well-defined access control policies can prevent mobile apps from being malicious. The problem is that those who oversee app market sites lack the mechanisms necessary to assess mobile app security. Because thousands of apps are constantly being added to or updated on mobile app market sites, these security testing mechanisms must be automated. This paper, therefore, introduces a new mechanism for testing mobile app security, using white-box testing in a way that is compatible with Bring Your Own Device (BYOD) working environments. This framework will benefit end-users, organizations that oversee app markets, and employers who implement the BYOD trend.

• Journal of IKEEE
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• v.23 no.2
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• pp.448-453
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• 2019

Under the conflicting objectives of maximum user satisfaction and fast launching, there exist great needs for automated mobile app testing. In automated app testing, detection of usage-state changes is one of the most important issues for minimizing human intervention and testing of various usage scenarios. Because conventional approaches utilizing pre-collected training examples can not handle the rapid evolution of apps, we propose a novel method detecting changes in usage-state through graph-entropy. In the proposed method, widgets in a screen shot are recognized through DNNs and 'onverted graphs. We compared the performance of the proposed method with a SIFT (Scale-Invariant Feature Transform) based method on 20 real-world apps. In most cases, our method achieved superior results, but we found some situations where further improvements are required.

• Journal of KIISE
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• v.41 no.12
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• pp.1013-1017
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• 2014

A variety of applications that are accessible through app markets provide useful features and functions. However, those applications can present many GUI bugs due to the deficiency of testing processes. Even though various approaches have been developed for mobile app testing, GUI bugs in applications are still difficult to be identified due to the absence of efficiency, lack of automation, and necessity of access to the source code. In this paper, we propose an automated black-box testing method for efficient GUI bug detection. Our experimental results show that the proposed method achieves better code coverage and uncovers GUI bugs when compared with existing black-box testing called Monkey.