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Combining Automated GUI Exploration of Android apps with Capture and Replay through Machine Learning.

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Title: Combining Automated GUI Exploration of Android apps with Capture and Replay through Machine Learning.
Authors: Amalfitano, Domenico1, Riccio, Vincenzo1, Amatucci, Nicola1, Simone, Vincenzo De1, Fasolino, Anna Rita1 annarita.fasolino@unina.it
Source: Information & Software Technology. Jan2019, Vol. 105, p95-116. 22p.
Subjects: Mobile apps, Android (Operating system), Machine learning, Software engineering, Reverse engineering
Abstract: Abstract Context Automated GUI Exploration Techniques have been widely adopted in the context of mobile apps for supporting critical engineering tasks such as reverse engineering, testing, and network traffic signature generation. Although several techniques have been proposed in the literature, most of them fail to guarantee the exploration of relevant parts of the applications when GUIs require to be exercised with particular and complex input event sequences. We refer to these GUIs as Gate GUIs and to the sequences required to effectively exercise them as Unlocking GUI Input Event Sequences. Objective In this paper, we aim at proposing a GUI exploration approach that exploits the human involvement in the automated process to solve the limitations introduced by Gate GUIs, without requiring the preliminary configuration of the technique or the user involvement for the entire duration of the exploration process. Method We propose juGULAR, a Hybrid GUI Exploration Technique combining Automated GUI Exploration with Capture and Replay. Our approach is able to automatically detect Gate GUIs during the app exploration by exploiting a Machine Learning approach and to unlock them by leveraging input event sequences provided by the user. We implement juGULAR in a modular software architecture that targets the Android mobile platform. We evaluate the performance of juGULAR by an experiment involving 14 real Android apps. Results The experiment shows that the hybridization introduced by juGULAR allows to improve the exploration capabilities in terms of Covered Activities, Covered Lines of Code, and generated Network Traffic Bytes at a reasonable manual intervention cost. The experimental results also prove that juGULAR is able to outperform the state-of-the-practice tool Monkey. Conclusion We conclude that the combination of Automated GUI Exploration approaches with Capture and Replay techniques is promising to achieve a thorough app exploration. Machine Learning approaches aid to pragmatically integrate these two techniques. [ABSTRACT FROM AUTHOR]
Copyright of Information & Software Technology is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
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DbLabel: Engineering Source
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PubType: Academic Journal
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  Data: Combining Automated GUI Exploration of Android apps with Capture and Replay through Machine Learning.
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  Data: <searchLink fieldCode="DE" term="%22Mobile+apps%22">Mobile apps</searchLink><br /><searchLink fieldCode="DE" term="%22Android+%28Operating+system%29%22">Android (Operating system)</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Software+engineering%22">Software engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Reverse+engineering%22">Reverse engineering</searchLink>
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  Data: Abstract Context Automated GUI Exploration Techniques have been widely adopted in the context of mobile apps for supporting critical engineering tasks such as reverse engineering, testing, and network traffic signature generation. Although several techniques have been proposed in the literature, most of them fail to guarantee the exploration of relevant parts of the applications when GUIs require to be exercised with particular and complex input event sequences. We refer to these GUIs as Gate GUIs and to the sequences required to effectively exercise them as Unlocking GUI Input Event Sequences. Objective In this paper, we aim at proposing a GUI exploration approach that exploits the human involvement in the automated process to solve the limitations introduced by Gate GUIs, without requiring the preliminary configuration of the technique or the user involvement for the entire duration of the exploration process. Method We propose juGULAR, a Hybrid GUI Exploration Technique combining Automated GUI Exploration with Capture and Replay. Our approach is able to automatically detect Gate GUIs during the app exploration by exploiting a Machine Learning approach and to unlock them by leveraging input event sequences provided by the user. We implement juGULAR in a modular software architecture that targets the Android mobile platform. We evaluate the performance of juGULAR by an experiment involving 14 real Android apps. Results The experiment shows that the hybridization introduced by juGULAR allows to improve the exploration capabilities in terms of Covered Activities, Covered Lines of Code, and generated Network Traffic Bytes at a reasonable manual intervention cost. The experimental results also prove that juGULAR is able to outperform the state-of-the-practice tool Monkey. Conclusion We conclude that the combination of Automated GUI Exploration approaches with Capture and Replay techniques is promising to achieve a thorough app exploration. Machine Learning approaches aid to pragmatically integrate these two techniques. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Information & Software Technology is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=132853604
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        Text: English
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              Text: Jan2019
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