Abstract: The methods and systems are directed to automated computer analysis and machine learning. Specifically, the systems and methods for using machine learning to generate fault prediction models and applying the fault prediction models to logical circuits to optimize test point insertion determinations and optimize fault detection in the logical circuit. Disclosed are methods and systems that that generates training data from training circuits (and optionally generate training circuits), trains a learning segment (which may include an artificial neural network (ANN)) using the training data. The learning segment (once trained) generates fault prediction models to predict the quality of a TP inserted on a given circuit location and optimize TPI for a given circuit. The methods and systems described provide computational (CPU/processing) time advantages and precision over conventional methods.