Reducing Test Execution Cost of Integrated, Heterogeneous Systems Using Continuous Test Data
Integrated, heterogeneous systems are comprehensively tested to verify whether their performance specifications fall within some acceptable ranges. However, explicitly testing every manufactured instance against all of its specifications can be expensive due to the complex requirements for test setup, stimulus application, and response measurement. To reduce manufacturing test cost, we have developed a methodology that uses binary decision forests and several test-specific enhancements for identifying redundant tests of an integrated system. Feasibility is empirically demonstrated using test data from over 70 000 manufactured instances of an in-production microelectromechanical system accelerometer, and over 4500 manufactured instances of an RF transceiver. Through our analysis, we have shown that the expensive cold-mechanical test of the accelerometer and nine out of the 22 RF tests of the transceiver are likely redundant.
binary decision forest integrated system test statistical learning test compaction