Abstract: A machine learning (ML) model is created via training or re-training one or more ML algorithms using past release(s) data (e.g., data comprising of requirements and corresponding test cases). The ML model comprises various clusters and these clusters are dynamically created every time when the ML model is trained (or retrained). One or more requirements exist in each cluster, and each requirement has one or more test cases associated with it. New requirements are mapped to a particular cluster and then test cases are compared against a universe of other test cases to determine whether to add a particular test case to a list of test cases that test the new requirement.