Although the company already had mature coverage of more than 800 global and local automated tests, the ecosystem required a new level of stabilization. The challenge was to optimize execution performance and component-mapping engineering, reducing maintenance cost to ensure full reliability of the CI/CD pipelines.
The goal of the consultancy was to improve the test automation framework with AI support to refactor legacy code. The focus included identifying bottlenecks and bad practices, mapping architecture/test-writing issues, creating a refactoring prompt for an AI code assistant, and proposing technical quick wins to stabilize the base and prepare it for the future.
The result was the delivery of an AI Agent (Copilot) for refactoring, which implemented improvements in web element mapping, implicit waits, layered code reorganization, and automation driven by User Stories and BDD. This solution accelerated the evolution of the legacy code and stabilized automation.
