A testability growth model based on evidential reasoning with nonlinear optimization

Testability growth is a process that aims to improve the testability level of the equipment via identifying and removing the testability design defects (TDDs). The establishment of the existing testability growth model (TGM) needs to consider a variety of factors, it's difficult to describe it accurately. To solve this problem, a TGM based on evidential reasoning (ER) method with nonlinear optimization is studied in this paper. According to the growth test data that can achieve the testability growth tracking and predicting. To estimate the parameters of the TGM accurately by using the mean square error (MSE). Finally, growth test data of a stable tracking platform is used to verify the validity of the model. The results show that the tracking accuracy is in the order of 0.0013 magnitude.