SummaryCovering all the possible paths of the graphical user interface (GUI) with test scripts would take too much effort and result in serious maintenance issues. We propose complementing scripted testing with scriptless test automation using the open‐source testar tool. This paper gives a comprehensive overview of testar and its latest extensions together with the ongoing and future research. With this paper, we hope we can help and encourage other researchers to use testar for their GUI testing‐related research and pave the way for an international research agenda in GUI testing built upon stable and open‐source infrastructure.
Companies are facing constant pressure towards shorter release cycles while still maintaining a high level of quality. Agile development, continuous integration and testing are commonly used quality assurance techniques applied in industry. Increasing the level of test automation is a key ingredient to address the short release cycles. Testing at the graphical user interface (GUI) level is challenging to automate, and therefore many companies still do this manually. To help find solutions for better GUI test automation, academics are researching scriptless GUI testing to complement the script-based approach. In order to better match industrial problems with academic results, more academiaindustry collaborations for case-based evaluations are needed. This paper describes such an initiative to improve, transfer and integrate an academic scriptless GUI testing tool TESTAR into the CI pipeline of a Spanish company Prodevelop. The paper describes the steps taken, the outcome, the challenges, and some lessons learned for successful industryacademia collaboration.
Extended Reality (XR) systems are complex applications that have emerged in a wide variety of domains, such as computer games and medical practice. Testing XR software is mainly done manually by human testers, which implies a high cost in terms of time and money. Current automated testing approaches for XR systems consist of rudimentary capture and replay of scripts. However, this approach only works for simple test scenarios. Moreover, it is well-known that the scripts break easily each time the XR system is changed. There are research projects aimed at using autonomous agents that will follow scripted instructions to test XR functionalities. Nonetheless, using only scripted testing techniques, it is difficult and expensive to tackle the challenges of testing XR systems. This thesis is focus on the use of automated scriptless testing for XR systems. This way we help to reduce part of the manual testing effort and complement the scripted techniques.
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