Turning Manual Tasks into Actions: Assessing the Effectiveness of Gemini-Generated Selenium Tests

Large Language Models (LLMs) have introduced innovative avenues for automating software testing using prompts. Despite numerous studies on software testing automation, there remains limited understanding on the effectiveness of LLM-generated Selenium tests. In this paper, we investigate the effectiveness of Gemini to produce Selenium tests from manual tasks specifications and HyperText Markup Language (HTML) code snippets. By effectiveness, we mean if the generated Selenium tests are executable and functionally accurate (meeting intended behavior specified in a manual task). To do that, we specify eight manual tasks (involving tasks related to search, filter, navigation, and form submissions) and define 25 actions for each task, using HTML code extracted from 200 web pages. These tasks require the interaction of diverse User Interface (UI) components, such as search boxes and checkboxes. The results indicate that 87.5 % of the generated Selenium tests are executable and 51.5 % of them meet the intended behavior. Manual tasks involving interaction with modals presented the greatest challenges for test generation. While carousels and buttons achieved relatively high success rates, they still accounted for many of the post-correction fixes. These components-often dynamic or context-dependent-were among those where most errors occurred during test generation.