A one-pass test-selection method for maximizing test coverage

Test selection aims at achieving high test quality with low test cost. By selecting only a subset of tests that most effectively detect defects, test time can be reduced while ensuring test escape is minimized. In this work, a new one-pass test-selection method is described that efficiently identifies tests that maximize either fault-model coverage or an N-detect test metric. The proposed method analyzes and selects each test one at a time in a streaming fashion to save both time and memory. The method is applied to two industrial designs, namely an IBM ASIC and an NVIDIA GPU. Experiment results demonstrate that the new method selects tests with coverage that virtually matches a greedy algorithm (less than 0.01% coverage difference), but uses less time (reduced by 2X) and memory (reduced by 20X to 200X). Additional experiments performed on two ISCAS circuits also demonstrates the new method typically achieves higher coverage but uses less time (reduced by 7X to 30X) and memory (reduced by 140X) as compared to selecting tests using a linear programming based approach.