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Deep Neural Networks (DNNs) are used in a wide variety of applications.However, as in any software application, DNN-based apps are afflicted withbugs. Previous work observed that DNN bug fix patterns are different fromtraditional bug fix patterns. Furthermore, those buggy models are non-trivialto diagnose and fix due to inexplicit errors with several options to fix them.To support developers in locating and fixing bugs, we propose DeepDiagnosis, anovel debugging approach that localizes the faults, reports error symptoms andsuggests fixes for DNN programs. In the first phase, our technique monitors atraining model, periodically checking for eight types of error conditions.Then, in case of problems, it reports messages containing sufficientinformation to perform actionable repairs to the model. In the evaluation, wethoroughly examine 444 models -53 real-world from GitHub and Stack Overflow,and 391 curated by AUTOTRAINER. DeepDiagnosis provides superior accuracy whencompared to UMLUAT and DeepLocalize. Our technique is faster than AUTOTRAINERfor fault localization. The results show that our approach can supportadditional types of models, while state-of-the-art was only able to handleclassification ones. Our technique was able to report bugs that do not manifestas numerical errors during training. Also, it can provide actionable insightsfor fix whereas DeepLocalize can only report faults that lead to numericalerrors during training. DeepDiagnosis manifests the best capabilities of faultdetection, bug localization, and symptoms identification when compared to otherapproaches.

DeepDiagnosis: Automatically Diagnosing Faults and Recommending Actionable Fixes in Deep Learning Programs