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See detailFine-grained Code Coverage Measurement in Automated Black-box Android Testing
Pilgun, Aleksandr UL; Gadyatskaya, Olga UL; Zhauniarovich, Yury et al

in ACM Transactions on Software Engineering and Methodology (2020), 29(4), 1-35

Today, there are millions of third-party Android applications. Some of them are buggy or even malicious. To identify such applications, novel frameworks for automated black-box testing and dynamic ... [more ▼]

Today, there are millions of third-party Android applications. Some of them are buggy or even malicious. To identify such applications, novel frameworks for automated black-box testing and dynamic analysis are being developed by the Android community. Code coverage is one of the most common metrics for evaluating effectiveness of these frameworks. Furthermore, code coverage is used as a fitness function for guiding evolutionary and fuzzy testing techniques. However, there are no reliable tools for measuring fine-grained code coverage in black-box Android app testing. We present the Android Code coVerage Tool, ACVTool for short, that instruments Android apps and measures code coverage in the black-box setting at class, method and instruction granularity. ACVTool has successfully instrumented 96.9% of apps in our experiments. It introduces a negligible instrumentation time overhead, and its runtime overhead is acceptable for automated testing tools. We demonstrate practical value of ACVTool in a large-scale experiment with Sapienz, a state-of-art automated testing tool. Using ACVTool on the same cohort of apps, we have compared different coverage granularities applied by Sapienz in terms of the found amount of crashes. Our results show that none of the applied coverage granularities clearly outperforms others in this aspect. [less ▲]

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See detailDissecting Android Cryptocurrency Miners
Dashevskyi, Stanislav UL; Zhauniarovich, Yury; Gadyatskaya, Olga UL et al

in CODASPY '20: Tenth ACM Conference on Data and Application Security and Privacy, New Orleans LA USA, March 2020 (2020, March)

Cryptojacking applications pose a serious threat to mobile devices. Due to the extensive computations, they deplete the battery fast and can even damage the device. In this work we make a step towards ... [more ▼]

Cryptojacking applications pose a serious threat to mobile devices. Due to the extensive computations, they deplete the battery fast and can even damage the device. In this work we make a step towards combating this threat. We collected and manually verified a large dataset of Android mining apps. In this paper, we analyze the gathered miners and identify how they work, what are the most popular libraries and APIs used to facilitate their development, and what static features are typical for this class of applications. Further, we analyzed our dataset using VirusTotal. The majority of our samples is considered malicious by at least one VirusTotal scanner, but 16 apps are not detected by any engine; and at least 5 apks were not seen previously by the service. Mining code could be obfuscated or fetched at runtime, and there are many confusing miner-related apps that actually do not mine. Thus, static features alone are not sufficient for miner detection.We have collected a feature set of dynamic metrics both for miners and unrelated benign apps, and built a machine learning-based tool for dynamic detection. Our BrenntDroid tool is able to detect miners with 95% of accuracy on our dataset. [less ▲]

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See detailDEMO: An Effective Android Code Coverage Tool
Pilgun, Aleksandr UL; Gadyatskaya, Olga UL; Dashevskyi, Stanislav UL et al

Poster (2018, October 15)

The deluge of Android apps from third-party developers calls for sophisticated security testing and analysis techniques to inspect suspicious apps without accessing their source code. Code coverage is an ... [more ▼]

The deluge of Android apps from third-party developers calls for sophisticated security testing and analysis techniques to inspect suspicious apps without accessing their source code. Code coverage is an important metric used in these techniques to evaluate their effectiveness, and even as a fitness function to help achieving better results in evolutionary and fuzzy approaches. Yet, so far there are no reliable tools for measuring fine-grained bytecode coverage of Android apps. In this work we present ACVTool that instruments Android apps and measures the smali code coverage at the level of classes, methods, and instructions. Tool repository: https://github.com/pilgun/acvtool [less ▲]

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See detailThe Influence of Code Coverage Metrics on Automated Testing Efficiency in Android
Dashevskyi, Stanislav UL; Gadyatskaya, Olga UL; Pilgun, Aleksandr UL et al

Poster (2018, October)

Code coverage is an important metric that is used by automated Android testing and security analysis tools to guide the exploration of applications and to assess efficacy. Yet, there are many different ... [more ▼]

Code coverage is an important metric that is used by automated Android testing and security analysis tools to guide the exploration of applications and to assess efficacy. Yet, there are many different variants of this metric and there is no agreement within the Android community on which are the best to work with. In this paper, we report on our preliminary study using the state-of-the-art automated test design tool Sapienz. Our results suggest a viable hypothesis that combining different granularities of code coverage metrics can be beneficial for achieving better results in automated testing of Android applications. [less ▲]

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See detailA Screening Test for Disclosed Vulnerabilities in FOSS Components
Dashevskyi, Stanislav UL; Brucker, Achim D.; Massacci, Fabio

in IEEE Transactions on Software Engineering (2018)

Detailed reference viewed: 80 (8 UL)