This paper reviews the data mining methodologies [1]-[4] proposed for functional test content optimization where tests are sequences of instructions or transactions. Basic machine learning concepts and the key ideas of these methodologies are explained. Challenges for implementing these methodologies in practice are illustrated. Promises are demonstrated through experimental results based on industrial verification settings.