Continuous Integration (CI) has become an integral part of modern software development, with platforms like GitHub Actions (GHA) enabling automated testing across multiple CI jobs targeting different environments (e.g., operating systems or language versions). However, many test cases often yield identical outcomes (pass or fail) across jobs, which can lead to redundant executions, longer workflows, and inefficient resource usage. For example, a typical GHA workflow might rerun the same test set on Linux, Windows, and macOS jobs, despite producing similar results. In this study, we aim to investigate how test cases are distributed across jobs in multi-job GHA workflows and examine the extent and consistency of redundant test executions. To achieve our objective, we will analyze a large dataset of open-source GitHub projects using GHA, employing static analysis of YAML configuration files and logs to identify test allocation, overlapping patterns, test outcome consistency, and estimate potential time savings. The findings of this study will offer insight into current testing practices in GHA and suggest opportunities to optimize workflows by reducing unnecessary duplication, improving both resource and cost efficiency.