Design for testing is becoming a critical stage of the design process. This procedure helps detect electrical faults on a digital circuit and it is of paramount importance in safety-critical applications. Automatic Test Pattern Generation is the traditional algorithmic approach capable of finding all the test pattern sequences that detect the presence of electrical faults. Unfortunately, due to its NP-Complete nature the decision problem requires backtracking before converging to a solution. Even though heuristics have been developed to decrease the number of backtracks this remains the bottleneck when dealing with industrial-scale designs. To address this problem, we introduce the concept of “Automatic Test Pattern Prediction” (ATPP) that leverages the power of deep learning to predict test patterns instead of generating them.