Optimal selection of tests is an important problem that arises in design for testability for complex electronic systems.Firstly

from the perspective of test tolerance

the reason of how tests produce miss detection and false alarm is analyzed.Then

a new mathematic model for the problem of test selection in the presence of imperfect tests is developed.It consists of minimizing the sum of test cost

miss detection cost and false alarm cost

subject to lower bound constraints on fault detection rate and fault isolation rate.To optimize the model

an improved quantum-inspired evolutionary algorithm is proposed.It is formed by making some improvements to an extant algorithm that has been used for optimal selection of perfect tests

including population initialization

fitness calculation and the strategy of population evolution.Finally

two simulation examples are used to validate the effectiveness and superiority of the solution method and the model.