DEFault uses hierarchical random forest classifiers on runtime and static code features to detect and categorize faults in DNN programs, reaching 94% detection and 63% diagnosis on a 52-program real-world benchmark.
Predicting line-level defects by capturing code contexts with hierarchical transformers,
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Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification
DEFault uses hierarchical random forest classifiers on runtime and static code features to detect and categorize faults in DNN programs, reaching 94% detection and 63% diagnosis on a 52-program real-world benchmark.