Suspect Set Prediction in RTL Bug Hunting
Neil Veiraa, Zissis Poulosb and Andreas Venerisc
Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada
anveira@eecg.toronto.edu
bzpoulos@eecg.toronto.edu
cveneris@eecg.toronto.edu
ABSTRACT
We propose a framework for predicting erroneous design components from partially observed solution sets that are found through automated debugging tools. The proposed method involves learning design component dependencies by using historical debugging data and representing these dependencies by means of a probabilistic graph. Using this representation, one can run a debugging tool non‐exhaustively, obtain a partial set of potentially erroneous components and then predict the remaining by applying a cost‐effective belief propagation pass. The method can reduce debugging runtime when it comes to multiple debugging sessions by 15x on the average while achieving a 91% average prediction accuracy.
