Adaptive test optimization through real time learning of test effectiveness

Production test suites include a large number of redundant test patterns due to the inclusion of multiple test types with overlapping defect detection and the use of simple fault models for test generation. Identification and elimination of ineffective test patterns promises a significant reduction in test cost. This paper proposes a test framework that learns, without extensive data collection and at no additional test time, the effectiveness of individual test patterns during production testing by getting defect detection feedback from a dynamic test flow. The proposed technique is further capable of adapting to changes in the underlying defect mechanisms by tracking the defect detection trend of test patterns.