Mobolic: An automated approach to exercising mobile application GUIs using symbiosis of online testing technique and customated input generation

Arnatovich, Yauhen Leanidavich; Wang, Lipo; Ngo, Ngoc Minh; Soh, Chee Kiong

doi:10.1002/spe.2564

Cited by 9 publications

(4 citation statements)

References 61 publications

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“…Based on Figure 8, each of the tools compared complements the others in crash detection and has its advantages. DroidbotX triggered an average of 18 unique crashes in 14 apps, followed by Sapienz (16), Stoat (14), Droidbot (12), Humanoid (12), and Android Monkey (11). Like activity coverage, Android Monkey remains the same as it has the least capacity to detect crashes due to its exploratory approach that generates a lot of ineffective and redundant events.…”

Section: Resultsmentioning

confidence: 99%

“…The development of GUI test cases usually takes a lot of time and effort because of their non-trivial structures and highly interactive nature of GUIs. Android apps [11,12] usually possess many states and transitions, which can lead to an arduous testing process and poor testing performance for large apps. Over the past decade, Android test generation tools have been developed to automate user interaction and system interaction as inputs [13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

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DroidbotX: Test Case Generation Tool for Android Applications Using Q-Learning

2021

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Android applications provide benefits to mobile phone users by offering operative functionalities and interactive user interfaces. However, application crashes give users an unsatisfactory experience, and negatively impact the application’s overall rating. Android application crashes can be avoided through intensive and extensive testing. In the related literature, the graphical user interface (GUI) test generation tools focus on generating tests and exploring application functions using different approaches. Such tools must choose not only which user interface element to interact with, but also which type of action to be performed, in order to increase the percentage of code coverage and to detect faults with a limited time budget. However, a common limitation in the tools is the low code coverage because of their inability to find the right combination of actions that can drive the application into new and important states. A Q-Learning-based test coverage approach developed in DroidbotX was proposed to generate GUI test cases for Android applications to maximize instruction coverage, method coverage, and activity coverage. The overall performance of the proposed solution was compared to five state-of-the-art test generation tools on 30 Android applications. The DroidbotX test coverage approach achieved 51.5% accuracy for instruction coverage, 57% for method coverage, and 86.5% for activity coverage. It triggered 18 crashes within the time limit and shortest event sequence length compared to the other tools. The results demonstrated that the adaptation of Q-Learning with upper confidence bound (UCB) exploration outperforms other existing state-of-the-art solutions.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

DroidbotX: Test Case Generation Tool for Android Applications Using Q-Learning

2021

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“…In addition, another work Mobolic [42] uses symbolic execution to extract the input constraints in app code and utilizes a solver to generate valid input. However, many input checks are enforced at the server side of Android apps.…”

Section: A Input Generation In Testing Android Appsmentioning

confidence: 99%

TextExerciser: Feedback-driven Text Input Exercising for Android Applications

Zhang

Yang

et al. 2020

2020 IEEE Symposium on Security and Privacy (SP)

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Dynamic analysis of Android apps is often used together with an exerciser to increase its code coverage. One big obstacle in designing such Android app exercisers comes from the existence of text-based inputs, which are often constrained by the nature of the input field, such as the length and character restrictions.In this paper, we propose TextExerciser, an iterative, feedback-driven text input exerciser, which generates text inputs for Android apps. Our key insight is that Android apps often provide feedback, called hints, for malformed inputs so that our system can utilize such hints to improve the input generation.We implemented a prototype of TextExerciser and evaluated it by comparing TextExerciser with state-of-the-art exercisers, such as The Monkey and DroidBot. Our evaluation shows that TextExerciser can achieve significantly higher code coverage and trigger more sensitive behaviors than these tools. We also combine TextExerciser with dynamic analysis tools and show they are able to detect more privacy leaks and vulnerabilities with TextExerciser than with existing exercisers. Particularly, existing tools, under the help of TextExerciser, find several new vulnerabilities, such as one user credential leak in a popular social app with more than 10,000,000 downloads.

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“…The development of GUI test cases usually takes a great deal of times and effort due to their non-trivial structures and the highly interactive nature of GUIs. Android apps [8,9] usually possess numerous states and transitions, which can lead to an arduous testing process and poor testing performance. For the past decade, Android test input generation tools have been developed to automate user interactions and allow system interactions as inputs [10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

An Empirical Analysis of Test Input Generation Tools for Android Apps through a Sequence of Events

et al. 2020

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Graphical User Interface (GUI) testing of Android apps has gained considerable interest from the industries and research community due to its excellent capability to verify the operational requirements of GUI components. To date, most of the existing GUI testing tools for Android apps are capable of generating test inputs by using different approaches and improve the Android apps’ code coverage and fault detection performance. Many previous studies have evaluated the code coverage and crash detection performances of GUI testing tools in the literature. However, very few studies have investigated the effectiveness of the test input generation tools, especially in the events sequence length of the overall test coverage and crash detection. The event sequence length generally shows the number of steps required by the test input generation tools to detect a crash. It is critical to highlight its effectiveness due to its significant effects on time, testing effort, and computational cost. Thus, this study evaluated the effectiveness of six test input generation tools for Android apps that support the system events generation on 50 Android apps. The generation tools were evaluated and compared based on the activity coverage, method coverage, and capability in detecting crashes. Through a critical analysis of the results, this study identifies the diversity and similarity of test input generation tools for Android apps to provide a clear picture of the current state of the art. The results revealed that a long events sequence performed better than a shorter events sequence. However, a long events sequence led to a minor positive effect on the coverage and crash detection. Moreover, the study showed that the tools achieved less than 40% of the method coverage and 67% of the activity coverage.

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Mobolic: An automated approach to exercising mobile application GUIs using symbiosis of online testing technique and customated input generation

Cited by 9 publications

References 61 publications

DroidbotX: Test Case Generation Tool for Android Applications Using Q-Learning

DroidbotX: Test Case Generation Tool for Android Applications Using Q-Learning

TextExerciser: Feedback-driven Text Input Exercising for Android Applications

An Empirical Analysis of Test Input Generation Tools for Android Apps through a Sequence of Events

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