Developed and integrated a Pytest coverage reporting enhancement into the databricks-ai-bridge repository, focusing on improving the CI/CD pipeline’s quality assurance process. Leveraging Python and Pytest, the work enabled automatic surfacing of test coverage metrics directly within CI logs, providing immediate visibility into test health for the team. This approach facilitated data-driven decision-making and improved risk management for upcoming releases by making code quality assessment more transparent. The enhancement established a foundation for future coverage-based quality gates, centralizing coverage data and streamlining QA feedback cycles. The month’s efforts centered on measurable quality improvements rather than bug fixes or feature expansion.