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EAGLE: Creating Equivalent Graphs to Test Deep Learning Libraries
2022
International Conference on Software Engineering
Testing deep learning (DL) software is crucial and challenging. Recent approaches use differential testing to cross-check pairs of implementations of the same functionality across different libraries. Such approaches require two DL libraries implementing the same functionality, which is often unavailable. In addition, they rely on a high-level library, Keras, that implements missing functionality in all supported DL libraries, which is prohibitively expensive and thus no longer maintained. To
doi:10.1145/3510003.3510165
dblp:conf/icse/WangLQP022
fatcat:iwwm3ph6xrauhccatusbiaumaq