GUICop: Approach and toolset for specification‐based GUI testing

Hammoud, Dalal; Zaraket, Fadi A.; Masri, Wes

doi:10.1002/stvr.1642

Cited by 2 publications

(2 citation statements)

References 27 publications

(68 reference statements)

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“…Initial specification-based GUI test oracle studies used operators with first-order logic semantics to describe the test oracle [45], [46]. Subsequently, GUICOP [47], [48] used a custom specification language with variables, properties, constraints, and relational operators based on propositional logic. These relational operators rely on arithmetic comparisons to describe GUI widgets relative to each other, such as a GUI widget residing next to another widget.…”

Section: ) Automated Gui Test Oraclesmentioning

confidence: 99%

Fast Witness Generation for Readable GUI Test Scenarios via Generalized Experience Replay

Koroglu¹,

Şen²

2022

IEEE Access

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Verifying the functional behavior of graphical user interface (GUI) applications is essential for reducing post-release issues. In practice, a developer/tester performs this verification by executing a sequence of GUI actions and then witnessing the expected behavior on the GUI screen. An automated witness generator facilitates the verification process. However, creating an unambiguous and monitorable specification for the correct behavior and then generating the correct GUI actions to trigger that behavior is challenging. In this study, we propose FARLEAD2, an automated witness generator that uses unambiguous, monitorable, and easy-to-read staged test scenarios (STSs) to specify expected behavior. FARLEAD2 maximizes its effectiveness and performance using generalized experienced replay (GER) to exploit the experience gathered from previously witnessed scenarios on new, unwitnessed test scenarios. To the best of our knowledge, STS and GER are novel improvements to GUI testing. Our evaluation of Android GUI applications shows that FARLEAD2 effectively generates a witness 95.7 times out of 100 and does it in 520 seconds, on average, indicating that FARLEAD2 is approximately 65 percent faster and 6.3 percent more effective than its best predecessor.

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Section: ) Automated Gui Test Oraclesmentioning

confidence: 99%

Fast Witness Generation for Readable GUI Test Scenarios via Generalized Experience Replay

Koroglu¹,

Şen²

2022

IEEE Access

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“…For instance, Memon and Soffa presented a regression testing technique for the graphical user interface (GUI) of a desktop application [52], while Brooks and Memon showed how to leverage user interaction profiles to guide GUI testing [53]. Furthermore, Hammoud et al and Zaraket et al proposed GUICop [54,55], a GUI testing approach that utilizes a specification language for capturing information about the layout and appearance of GUI components. This enables functional test cases to tolerate differences in the execution environment in which they are run-for example a change of screen resolution-while also checking properties of the GUI itself, such as the appearance and relative positioning of certain elements.…”

Section: Maximummentioning

confidence: 99%

Automated visual classification of DOM‐based presentation failure reports for responsive web pages

Althomali

Kapfhammer

McMinn

2021

Software Testing Verif & Rel

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Since it is common for the users of a web page to access it through a wide variety of devices-including desktops, laptops, tablets and phones-web developers rely on responsive web design (RWD) principles and frameworks to create sites that are useful on all devices. A correctly implemented responsive web page adjusts its layout according to the viewport width of the device in use, thereby ensuring that its design suitably features the content. Since the use of complex RWD frameworks often leads to web pages with hardto-detect responsive layout failures (RLFs), developers employ testing tools that generate reports of potential RLFs. Since testing tools for responsive web pages, like ReDECHECK, analyse a web page representation called the Document Object Model (DOM), they may inadvertently flag concerns that are not human visible, thereby requiring developers to manually confirm and classify each potential RLF as a true positive (TP), false positive (FP), or non-observable issue (NOI)-a process that is time consuming and error prone. The conference version of this paper presented VISER, a tool that automatically classified three types of RLFs reported by ReDECHECK. Since VISER was not designed to automatically confirm and classify two types of RLFs that ReDECHECK's DOM-based analysis could surface, this paper introduces VERVE, a tool that automatically classifies all RLF types reported by ReDECHECK. Along with manipulating the opacity of HTML elements in a web page, as does VISER, the VERVE tool also uses histogram-based image comparison to classify RLFs in web pages. Incorporating both the 25 web pages used in prior experiments and 20 new pages not previously considered, this paper's empirical study reveals that VERVE's classification of all five types of RLFs frequently agrees with classifications produced manually by humans. The experiments also reveal that VERVE took on average about 4 s to classify any of the RLFs among the 469 reported by ReDECHECK. Since this paper demonstrates that classifying an RLF as a TP, FP, or NOI with VERVE, a publicly available tool, is less subjective and error prone than the same manual process done by a human web developer, we argue that it is well-suited for supporting the testing of complex responsive web pages.

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GUICop: Approach and toolset for specification‐based GUI testing

Cited by 2 publications

References 27 publications

Fast Witness Generation for Readable GUI Test Scenarios via Generalized Experience Replay

Fast Witness Generation for Readable GUI Test Scenarios via Generalized Experience Replay

Automated visual classification of DOM‐based presentation failure reports for responsive web pages

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