Recent advances in web agent technologies have enabled automated web browser operations through natural language instructions. While these technologies show promise for end-to-end test automation, significant challenges remain, such as uncertainty in test execution due to LLMs, increased execution time and cost, and decreased accuracy in identifying operation targets as web pages become more complex. To address these challenges, we propose XTestGen, which generates Gherkinformat test cases and JavaScript step definitions from natural language. XTestGen improves reproducibility by producing deterministic scripts, enhances maintainability through modular step reuse and scenario abstraction, and increases element identification accuracy in complex web pages using hierarchical tree exploration. Our evaluation shows that XTestGen enables abstraction and reuse in test generation and achieves higher accuracy in element identification than naive approaches. A demonstration video is available at: https://youtu.be/sQmsNCPGtPo

Exploratory testing is an important activity for improving software quality. Despite this, few studies have been conducted to support exploratory testing. One reason for this is the lack of available data on exploratory testing activities. We propose LatteArt, a tool to support developers in recording, visualizing and managing exploratory testing activities. LatteArt has a rich user interface and a variety of features to support exploratory testing, and is publicly available. We conducted a questionnaire with three developers to assess whether LatteArt addresses the challenges of exploratory testing. In our questionnaire, two or more developers indicated that LatteArt helps to address 14 of the 17 exploratory testing weaknesses identified in the existing literature. The test activity data recorded by LatteArt is easily accessible via REST APIs, allowing researchers to use the data and facilitate exploratory testing research. Our tool and demo are available at: https://github.com/latteart-org/latteart