Automated model-based Android GUI testing using multi-level GUI comparison criteria

Baek, Youngmin; Bae, Doo‐Hwan

doi:10.1145/2970276.2970313

Cited by 139 publications

(59 citation statements)

References 25 publications

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“…These models are usually represented as finite state machines that store the transitions between app window states. Such GUI models can be constructed dynamically [5], [6], [11]- [17] or statically [18]. Based on the GUI models, testing tools can generate events that can quickly navigate the app to unexplored states.…”

Section: B Automated Gui Test Generationmentioning

confidence: 99%

Humanoid: A Deep Learning-Based Approach to Automated Black-box Android App Testing

Yang

Guo

et al. 2019

2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE)

120

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Automated input generators are widely used for large-scale dynamic analysis and testing of mobile apps. Such input generators must constantly choose which UI element to interact with and how to interact with it, in order to achieve high coverage with a limited time budget. Currently, most input generators adopt pseudo-random or brute-force searching strategies, which may take very long to find the correct combination of inputs that can drive the app into new and important states.In this paper, we propose Humanoid, a deep learning-based approach to automated Android app testing. Our insight is that if we can learn from human-generated interaction traces, it is possible to generate human-like test inputs based on the visual information in the current UI state and the latest state transitions. We design and implement a deep neural network model to learn how end-users would interact with an app (specifically, which UI elements to interact with and how), and show that we can successfully generate human-like inputs for any new UI based on the learned model. We then apply the model to automated testing of Android apps and demonstrate that it is able to reach higher coverage, and faster as well, than the state-of-the-art test input generators.

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Section: B Automated Gui Test Generationmentioning

confidence: 99%

Humanoid: A Deep Learning-Based Approach to Automated Black-box Android App Testing

Yang

Guo

et al. 2019

2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE)

120

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“…Baek and Bae [19] proposed an automated testing framework for Android apps utilizing dynamic analysis. They implemented a set of multi-level GUI Comparison Criteria (GUICC) that provides the selection of multiple abstraction levels to improve GUI model generation.…”

Section: A Dynamic Approachesmentioning

confidence: 99%

“…However, due to the use of agile development processes by most developers in which the requirements and implementation are iterating rapidly from a version to another, modeling in such a situation is difficult [16], [18]. Hence, mobile apps model is often not available or of insufficient quality [19]. The model can be generated from the application's documentation [20] or sequences of actions observed in an application [16].…”

Section: Introductionmentioning

confidence: 99%

AMOGA: A Static-Dynamic Model Generation Strategy for Mobile Apps Testing

et al. 2019

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In the past few years, mobile devices have been increasingly replacing traditional computers as their capabilities such as CPU computation, memory, RAM size, and many more, are being enhanced almost to the level of conventional computers. These capabilities are being exploited by mobile apps developers to produce apps that offer more functionalities and optimized performance. To ensure acceptable quality and to meet their specifications (e.g., design), mobile apps need to be tested thoroughly. As the testing process is often tedious, test automation can be the key to alleviating such laborious activities. In the context of the Android-based mobile apps, researchers and practitioners have proposed many approaches to automate the testing process mainly on the creation of the test suite. Although useful, most existing approaches rely on reverse engineering a model of the application under test for test case creation. Often, such approaches exhibit a lack of comprehensiveness as the application model does not capture the dynamic behavior of the applications extensively due to the incompleteness of reverse engineering approaches. To address this issue, this paper proposes AMOGA, a strategy that uses a hybrid, static-dynamic approach for generating user interface model from mobile apps for modelbased testing. AMOGA implements a novel crawling technique that uses the event list of UI element associated with each event to dynamically exercise the events ordering at the run-time to explore the applications' behavior. An experimental evaluation was performed to assess the effectiveness of our strategy by measuring the code coverage and the fault detection capability through the use of mutation testing concept. Results of the experimental assessment showed that AMOGA represents an alternative approach for model-based testing of mobile apps by generating comprehensive models to improve the coverage of the applications. The strategy proved its effectiveness by achieving high code coverage and mutation score for different applications.

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“…Baek and Bae [22] define a comparison criterion for Android GUI states. AndroFrame uses the maximum comparison level described in this work, which makes our models as fine-grained as possible for black-box testing.…”

Section: Related Workmentioning

confidence: 99%

TCM: Test Case Mutation to Improve Crash Detection in Android

Koroglu

Şen

2018

Fundamental Approaches to Software Engineering

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GUI testing of mobile applications gradually became a very important topic in the last decade with the growing mobile application market. We propose Test Case Mutation (TCM) which mutates existing test cases to produce richer test cases. These mutated test cases detect crashes that are not previously detected by existing test cases. TCM differs from the well-known Mutation Testing (MT) where mutations are inserted in the source code of an Application Under Test (AUT) to measure the quality of test cases. Whereas in TCM, we modify existing test cases and obtain new ones to increase the number of detected crashes. Android applications take the largest portion of the mobile application market. Hence, we evaluate TCM on Android by replaying mutated test cases of randomly selected 100 AUTs from F-Droid benchmarks. We show that TCM is effective at detecting new crashes in a given time budget.

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Automated model-based Android GUI testing using multi-level GUI comparison criteria

Cited by 139 publications

References 25 publications

Humanoid: A Deep Learning-Based Approach to Automated Black-box Android App Testing

Humanoid: A Deep Learning-Based Approach to Automated Black-box Android App Testing

AMOGA: A Static-Dynamic Model Generation Strategy for Mobile Apps Testing

TCM: Test Case Mutation to Improve Crash Detection in Android

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