# Automatic Fault Detection for Deep Learning Programs Using Graph Transformations arXiv: https://arxiv.org/abs/2105.08095 ยท HTML: https://mlsanity.com/paper/2105.08095/ ## Key facts - Cited by 9 papers in the map (data through 14 Oct 2026). - Builds on 27 of its 49 references that are in the map. - First posted on arXiv 17 May 2021. ## Builds on (most cited first, 27 of 27) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014) - [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014) - [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015) - [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015) - [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016) - [Striving for Simplicity: The All Convolutional Net](https://mlsanity.com/paper/1412.6806/) (2014) - [DeepXplore: Automated Whitebox Testing of Deep Learning Systems](https://mlsanity.com/paper/1705.06640/) (2017) - [DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars](https://mlsanity.com/paper/1708.08559/) (2017) - [Learning to Represent Programs with Graphs](https://mlsanity.com/paper/1711.00740/) (2017) - [Deep Pyramidal Residual Networks](https://mlsanity.com/paper/1610.02915/) (2016) - [Activation Functions: Comparison of trends in Practice and Research for Deep Learning](https://mlsanity.com/paper/1811.03378/) (2018) - [An Analysis of Deep Neural Network Models for Practical Applications](https://mlsanity.com/paper/1605.07678/) (2016) - [On the Impact of the Activation Function on Deep Neural Networks Training](https://mlsanity.com/paper/1902.06853/) (2019) - [Taxonomy of Real Faults in Deep Learning Systems](https://mlsanity.com/paper/1910.11015/) (2019) - [Convolutional Neural Networks at Constrained Time Cost](https://mlsanity.com/paper/1412.1710/) (2014) - [A Comprehensive Study on Deep Learning Bug Characteristics](https://mlsanity.com/paper/1906.01388/) (2019) - [On Testing Machine Learning Programs](https://mlsanity.com/paper/1812.02257/) (2018) - [Understanding the Disharmony between Dropout and Batch Normalization by Variance Shift](https://mlsanity.com/paper/1801.05134/) (2018) - [Repairing Deep Neural Networks: Fix Patterns and Challenges](https://mlsanity.com/paper/2005.00972/) (2020) - [Identifying Implementation Bugs in Machine Learning based Image Classifiers using Metamorphic Testing](https://mlsanity.com/paper/1808.05353/) (2018) - [DeepEvolution: A Search-Based Testing Approach for Deep Neural Networks](https://mlsanity.com/paper/1909.02563/) (2019) - [Developing Bug-Free Machine Learning Systems With Formal Mathematics](https://mlsanity.com/paper/1706.08605/) (2017) - [Towards Principled Design of Deep Convolutional Networks: Introducing SimpNet](https://mlsanity.com/paper/1802.06205/) (2018) - [Deep Convolutional Neural Network Design Patterns](https://mlsanity.com/paper/1611.00847/) (2016) - [TFCheck : A TensorFlow Library for Detecting Training Issues in Neural Network Programs](https://mlsanity.com/paper/1909.02562/) (2019) - [Software Language Comprehension using a Program-Derived Semantics Graph](https://mlsanity.com/paper/2004.00768/) (2020) ## Built on by (most cited first, 9 of 9) - [Bugs in Machine Learning-based Systems: A Faultload Benchmark](https://mlsanity.com/paper/2206.12311/) (2022) - [Bug Characterization in Machine Learning-based Systems](https://mlsanity.com/paper/2307.14512/) (2023) - [Mutation-based Fault Localization of Deep Neural Networks](https://mlsanity.com/paper/2309.05067/) (2023) - [Debugging and Runtime Analysis of Neural Networks with VLMs (A Case Study)](https://mlsanity.com/paper/2503.17416/) (2025) - [Toward Debugging Deep Reinforcement Learning Programs with RLExplorer](https://mlsanity.com/paper/2410.04322/) (2024) - [Why Attention Fails: A Taxonomy of Faults in Attention-Based Neural Networks](https://mlsanity.com/paper/2508.04925/) (2025) - [Path Analysis for Effective Fault Localization in Deep Neural Networks](https://mlsanity.com/paper/2310.18987/) (2023) - [An Empirical Study of Fault Localisation Techniques for Deep Learning](https://mlsanity.com/paper/2412.11304/) (2024) - [Hierarchical Fault Detection and Diagnosis for Transformer Architectures](https://mlsanity.com/paper/2604.28118/) (2026) ## Abstract (arXiv) Nowadays, we are witnessing an increasing demand in both corporates and academia for exploiting Deep Learning (DL) to solve complex real-world problems. A DL program encodes the network structure of a desirable DL model and the process by which the model learns from the training dataset. Like any software, a DL program can be faulty, which implies substantial challenges of software quality assurance, especially in safety-critical domains. It is therefore crucial to equip DL development teams with efficient fault detection techniques and tools. In this paper, we propose NeuraLint, a model-based fault detection approach for DL programs, using meta-modelling and graph transformations. First, we design a meta-model for DL programs that includes their base skeleton and fundamental properties. Then, we construct a graph-based verification process that covers 23 rules defined on top of the meta-model and implemented as graph transformations to detect faults and design inefficiencies in the generated models (i.e., instances of the meta-model). First, the proposed approach is evaluated by finding faults and design inefficiencies in 28 synthesized examples built from common problems reported in the literature. Then NeuraLint successfully finds 64 faults and design inefficiencies in 34 real-world DL programs extracted from Stack Overflow posts and GitHub repositories. The results show that NeuraLint effectively detects faults and design issues in both synthesized and real-world examples with a recall of 70.5 % and a precision of 100 %. Although the proposed meta-model is designed for feedforward neural networks, it can be extended to support other neural network architectures such as recurrent neural networks. Researchers can also expand our set of verification rules to cover more types of issues in DL programs.