Software testing in highly configurable environments encounters major challenges because of the need for manual resource file management and the diffculty of selecting environments. This research introduces a framework for automating testing infrastructure with EnvContainer, a new system aimed at removing the need for manual work in configuring environments and providing resources. The study addresses two key issues. First, it looks at the time consuming and error-prone process of manually downloading and specifying customer-specific user interface configurations across different testing environments. Second, it addresses the complexity involved in manually selecting environment. The proposed EnvContainer solution has four-component architecture. It includes an Environment Manager, a Dialogue Manager, a Path Interceptor, and a pytest integrator. The Environment Manager handles environmental metadata and lifecycle operations. The Dialogue Manager handles downloading and extracting dialogue files from the artifact server. Path Interceptor creates symbolic links on Windows to avoid configuration files copying overhead. The system works well with pytest through hook-based integration. This allows for automatic environment switching during sequential test runs, without needing to change the existing test code. This research includes a formal framework implementation providing immediate industrial applicability. The validation confirmed that EnvContainer correctly automates environment selection and switching. Practical limitations include the need for manual test annotation with pytest markers, and the deferral of Linux platform testing to future work. Future research directions include reinforcement learning integration for predictive environment selection. This work establishes a foundation for intelligent automated testing in complex, multi-platform development environments.