Making GUI Testing Practical: Bridging the Gaps

Aho, Pekka; Suarez, Matias; Memon, Atif M.; Kanstrén, Teemu

doi:10.1109/itng.2015.77

Cited by 6 publications

(3 citation statements)

References 30 publications

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“…The models could also serve as visual reference models for testers or users, or be valuable for model-based GUI testing (MBGT) tools [3]. MBGT has not been widely adopted, because creating the models requires modelling expertise and a lot of effort [7]. If we can infer even initial models, these problems might be (partially) solved.…”

Section: Introductionmentioning

confidence: 99%

State Model Inference Through the GUI Using Run-Time Test Generation

Mulders

Valdes

́os

et al. 2022

Research Challenges in Information Science

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Software testing is an important part of engineering trustworthy information systems. End-to-end testing through Graphical User Interface (GUI) can be done manually, but it is a very time consuming and costly process. There are tools to capture or manually define scripts for automating regression testing through a GUI, but the main challenge is the high maintenance cost of the scripts when the GUI changes. In addition, GUIs tend to have a large state space, so creating scripts to cover all the possible paths and defining test oracles to check all the elements of all the states would be an enormous effort. This paper presents an approach to automatically explore a GUI while inferring state models that are used for action selection in run-time GUI test generation, implemented as an extension to the open source TESTAR tool. As an initial validation, we experiment on the impact of using various state abstraction mechanisms on the model inference and the performance of the implemented action selection algorithm based on the inferred model. Later, we analyse the challenges and provide future research directions on model inference and scriptless GUI testing.

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Section: Introductionmentioning

confidence: 99%

State Model Inference Through the GUI Using Run-Time Test Generation

Mulders

Valdes

́os

et al. 2022

Research Challenges in Information Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…Nowadays test automation is becoming increasingly popular among the software engineering community in recent times [11][12][13]. Numerous testing techniques have been proposed for testing mobile apps in the past few years.…”

Section: Introductionmentioning

confidence: 99%

TEGDroid: Test Case Generation Approach for Android Apps Considering Context and GUI Events

Usman

Ibrahim

Salihu

2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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The advancement in mobile technologies has led to the production of mobile devices (e.g. smartphone) with rich innovative features. This has enabled the development of mobile applications that offer users an advanced and extremely localized context-aware content. The recent dependence of people on mobile applications for various computational needs poses a significant concern on the quality of mobile applications. In order to build a high quality and more reliable applications, there is a need for effective testing techniques to test the applications. Most existing testing technique focuses on GUI events only without sufficient support for context events. This makes it difficult to identify other defects in the changes that can be inclined by context in which an application runs. This paper presents an approach named TEGDroid for generating test case for Android Apps considering both context and GUI Events. The GUI and context events are identified through the static analysis of bytecode, and the analysis of app's permission from the XML file. An experiment was performed on real world mobile apps to evaluate TEGDroid. Our experimental results show that TEGDroid is effective in identifying context events and had 65%-91% coverage across the eight selected applications. To evaluate the fault detection capability of this approach, mutation testing was performed by introducing mutants to the applications. Results from the mutation analysis shows that 100% of the mutants were killed. This indicates that TEGDroid have the capability to detect faults in mobile apps.

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“…The model can be generated from the application's documentation [20] or sequences of actions observed in an application [16]. The reverse engineering approach has been used recently to generate the model automatically [16], [21]. To benefit from the MBT approach, there is a demand for techniques/tools to aid automated model generation from the mobile apps.…”

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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Making GUI Testing Practical: Bridging the Gaps

Cited by 6 publications

References 30 publications

State Model Inference Through the GUI Using Run-Time Test Generation

State Model Inference Through the GUI Using Run-Time Test Generation

TEGDroid: Test Case Generation Approach for Android Apps Considering Context and GUI Events

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

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