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With the wide use of Deep Learning (DL) systems, academy and industry beginto pay attention to their quality. Testing is one of the major methods ofquality assurance. However, existing testing techniques focus on the quality ofDL models but lacks attention to the core underlying inference engines (i.e.,frameworks and libraries). Inspired by the success stories of fuzz testing, wedesign a graph-based fuzz testing method to improve the quality of DL inferenceengines. This method is naturally followed by the graph structure of DL models.A novel operator-level coverage criterion based on graph theory is introducedand six different mutations are implemented to generate diversified DL modelsby exploring combinations of model structures, parameters, and data inputs. TheMonte Carlo Tree Search (MCTS) is used to drive DL model generation without atraining process. The experimental results show that the MCTS outperforms therandom method in boosting operator-level coverage and detecting exceptions. Ourmethod has discovered more than 40 different exceptions in three types ofundesired behaviors: model conversion failure, inference failure, outputcomparison failure. The mutation strategies are useful to generate new validtest inputs, by up to 8.2% more operator-level coverage on average and 8.6 moreexceptions captured.

Graph-Based Fuzz Testing for Deep Learning Inference Engine