Developed regression test coverage for the Comparative Workflows machine learning algorithm in the getsentry/sentry repository, focusing on validating the algorithm’s logic and performance. Leveraged Python for implementing tests that use Kullback-Leibler divergence and related metrics to compare actual outcomes against expected results, ensuring reproducible validation and higher deployment confidence. Applied skills in data analysis, machine learning, and testing to improve the reliability of ML workflow deployments. The work emphasized robust test design rather than bug fixing, resulting in safer and more predictable releases. This contribution enhanced test coverage and supported ongoing quality assurance for the Comparative Workflows project.