Worked on the Artelnics/opennn repository to enhance the reliability of model evaluation by developing comprehensive tests for dataset handling and CrossEntropyError computations. Focused on expanding test coverage across various data types, correlations, and error calculations, the work involved refactoring and hardening existing unit tests to improve robustness and correctness. Leveraged C++ and applied skills in data preprocessing, error metrics, and machine learning to ensure more reliable continuous integration signals and reduce production risks. By linking changes to specific commits, the approach improved traceability and facilitated easier code review and rollback, contributing to the overall stability of the codebase.