Online selection of effective functional test programs based on novelty detection

This paper proposes an online functional test selection approach based on novelty detection. Unlike other test selection methods, the idea of this paper is selecting novel functional tests to improve coverage from a large pool of available test programs before simulation. A graph based encoding scheme is developed to measure the similarity between test programs and map them into a set of feature vectors. We employ one-class SVM as the learning algorithm to detect novel tests to be simulated. While leaving the general test selection framework unchanged, the developed test program similarity measure can easily be tailored to specific applications and coverage targets based on existing simulation results. Experiments on a public domain MIPS processor design are presented to demonstrate the effectiveness of the approach.