CACOP-a random pattern testability analyzer

In this paper a new method called CACOP For the detection probability analyses of random test patterns is proposed. Considering computational complexity, CACOP is a compromise between O(n²) testability analyses like full-range cutting algorithm (FRCA) and linear time testability analyses like the controllability observability program (COP). By propagating bounds of controllabilities and observabilities, CACOP can determine the detection probability lower bound (DPLB) efficiently. The DPLBs derived by CACOP are potentially higher (and thus more accurate) than by FRCA; in addition, CACOP is computationally more efficient than FRCA. The conventional linear time testability analyses cannot guarantee the derivation of DPLBs. On the contrary, CACOP can achieve the goal with tolerable increase in computing complexity