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.
Deep Learning & Software Engineering: State of Research and Future Directions
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abstract
Given the current transformative potential of research that sits at the intersection of Deep Learning (DL) and Software Engineering (SE), an NSF-sponsored community workshop was conducted in co-location with the 34th IEEE/ACM International Conference on Automated Software Engineering (ASE'19) in San Diego, California. The goal of this workshop was to outline high priority areas for cross-cutting research. While a multitude of exciting directions for future work were identified, this report provides a general summary of the research areas representing the areas of highest priority which were discussed at the workshop. The intent of this report is to serve as a potential roadmap to guide future work that sits at the intersection of SE & DL.
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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.