Abstract: A first set of features may be extracted from a first integrated circuit (IC) design. A trained machine learning (ML) model may predict a set of ranked test-case configurations for the first IC design based on the first set of features. A test-case configuration may correspond to a count of scan chain input and output ports and a scan chain length value.

Abstract: Training data may be collected based on a set of test-case configurations for each integrated circuit (IC) design in a set of IC designs. The training data may include a set of features extracted from each IC design, and a count of test cycles required for achieving a target test coverage for each test-case configuration. A machine learning (ML) model may be trained using the training data to obtain a trained ML model. The trained ML model may be used to predict a set of ranked test-case configurations for a given IC design based on features extracted from the given IC design.