Coverage-Directed Test Generation Automated by Machine Learning -- A Review
ACM Transactions on Design Automation of Electronic Systems (TODAES)
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Biased random test generation is one of the most important methods for the verification of modern complex processors. As the complexity of processors grows, the bottleneck remains in generating suitable test programs that meet coverage metrics automatically. Many technologies have been proposed to implement the automatic feedback loop. In this paper, we introduce our coverage directed test generation scheme which combines traditional biased random test generation and genetic algorithms to feed back process. It is the first time we use our scheme in our real industrial processor verification independently and successfully without human intervention. The efficiency of our approach has been demonstrated by the practical results.