This paper presents an industrial experience applying random scriptless GUI testing to the Yoho web application developed by Marviq. The study was motivated by several key challenges faced by the company, including the need to optimise testing resources, explore how random testing can complement manual testing, and investigate new coverage metrics, such as “code smell coverage”, to assess software quality and maintainability. We conducted an experiment to explore the impact of the number and length of random GUI test sequences on traditional adequacy metrics, the complementarity of random with manual testing, and the relationship between code smell coverage and traditional code coverage. Using Testar for scriptless testing and SonarQube code smell identification, results show that longer random test sequences yielded better test adequacy metrics and increased code smell coverage. In addition, random testing offers promising efficiency in test coverage and detects unique smells that m anual testing might overlook. Additionally, including code smell coverage provides valuable insights into long-term code maintainability, revealing gaps that traditional metrics may not capture. These findings highlight the benefits of combining functional testing with metrics assessing code quality, particularly in resource-constrained environments.

Summary Covering 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.

TESTAR is a tool for automated testing via the GUI. It uses dynamic analysis during automated GUI exploration and generates the test sequences during the execution. TESTAR saves all kind of information about the tests in a Graph database that can be queried or traversed during or after the tests using a traversal language. Test sequences leading to a failure can be excessively long, making the root-cause analysis of the failure difficult. This paper proposes an initial approach to find the shortest path to reproduce an error found by TESTAR