What You See Is What You Test - Augmenting Software Testing with Computer Vision

Ramler, Rudolf; Ziebermayr, Thomas

doi:10.1109/icstw.2017.76

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…It is worth mentioning that we are not the first to evaluate a hardware using a camera. For example, Ramler and Ziebermayr (2017) used a camera-based testbed to verify the behaviour of a mechatronic system.…”

Section: Component Testmentioning

confidence: 99%

A Genetic Algorithm for HMI Test Infrastructure Fine Tuning

Rosenbauer

Stein

Hähner

2021

Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics

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Human machine interfaces (HMI) have become a part of our daily lives. They are an essential part of a variety of products ranging from computers over smart phones to home appliances. Customer's requirements for HMIs are rising and so does the complexity of the devices. Several years ago, many products had a rather simple HMI such as mere buttons. Nowadays lots of devices have screens that display complex text messages and a variety of objects such as icons. This leads to new challenges in testing, the goal of which it is to ensure quality and to find errors. We combine a genetic algorithm with computer vision techniques in order to solve two testing use cases located in the automated verification of displays. Our method has a low runtime and can be used on low budget equipment such as Raspberry Pi which reduces the operational cost in practice.

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Section: Component Testmentioning

confidence: 99%

A Genetic Algorithm for HMI Test Infrastructure Fine Tuning

Rosenbauer

Stein

Hähner

2021

Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics

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“…Alégroth et al [61] develop the JAutomate tool that combines image recognition with test script record and replay to reduce the automation cost of visual GUI testing [62]. Chang et al [63] apply computer vision techniques to enable conventional software testing to observe the states and events in physical world. Nguyen et al [64] develop the REMAUI approach where computer vision and optical character recognition techniques are applied to reverse engineer mobile application user interfaces.…”

Section: Computer Vision In Software Engineeringmentioning

confidence: 99%

GUI-Guided Test Script Repair for Mobile Apps

Pan

Pei

et al. 2020

IIEEE Trans. Software Eng.

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Graphical User Interface (GUI) testing is widely used to test mobile apps. As mobile apps are frequently updated and need repeated testing, to reduce the test cost, their test cases are often coded as scripts to enable automated execution using test harnesses/tools. When those mobile apps evolve, many of the test scripts, however, may become broken due to changes made to the app GUIs. While it is desirable that the broken scripts get repaired, doing it manually can be preventively expensive if the number of tests need repairing is large. We propose in this paper a novel approach named METER to repairing broken GUI test scripts automatically when mobile apps evolve. METER leverages computer vision techniques to infer GUI changes between two versions of a mobile app and uses the inferred changes to guide the repair of GUI test scripts. Since METER only relies on screenshots to repair GUI tests, it is applicable to apps targeting open or closed source mobile platforms. In experiments conducted on 22 Android apps and 6 iOS apps, repairs produced by METER helped preserve 63.7% and 38.8% of all the test actions broken by the GUI changes, respectively.

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What You See Is What You Test - Augmenting Software Testing with Computer Vision

Cited by 2 publications

References 6 publications

A Genetic Algorithm for HMI Test Infrastructure Fine Tuning

A Genetic Algorithm for HMI Test Infrastructure Fine Tuning

GUI-Guided Test Script Repair for Mobile Apps

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