# DeepFault: Fault Localization for Deep Neural Networks

arXiv: https://arxiv.org/abs/1902.05974 · HTML: https://mlsanity.com/paper/1902.05974/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Feb 2019.

## Built on by (most cited first, 30 of 30)

- [Software Engineering for AI-Based Systems: A Survey](https://mlsanity.com/paper/2105.01984/) (2021)
- [How to Certify Machine Learning Based Safety-critical Systems? A Systematic Literature Review](https://mlsanity.com/paper/2107.12045/) (2021)
- [NNrepair: Constraint-based Repair of Neural Network Classifiers](https://mlsanity.com/paper/2103.12535/) (2021)
- [A Survey on Machine Learning Techniques for Source Code Analysis](https://mlsanity.com/paper/2110.09610/) (2021)
- [DeepDiagnosis: Automatically Diagnosing Faults and Recommending Actionable Fixes in Deep Learning Programs](https://mlsanity.com/paper/2112.04036/) (2021)
- [DeepFD: Automated Fault Diagnosis and Localization for Deep Learning Programs](https://mlsanity.com/paper/2205.01938/) (2022)
- [AequeVox: Automated Fairness Testing of Speech Recognition Systems](https://mlsanity.com/paper/2110.09843/) (2021)
- [Machine Learning Robustness: A Primer](https://mlsanity.com/paper/2404.00897/) (2024)
- [ArchRepair: Block-Level Architecture-Oriented Repairing for Deep Neural Networks](https://mlsanity.com/paper/2111.13330/) (2021)
- [Mutation-based Fault Localization of Deep Neural Networks](https://mlsanity.com/paper/2309.05067/) (2023)
- [NEUROSPF: A tool for the Symbolic Analysis of Neural Networks](https://mlsanity.com/paper/2103.00124/) (2021)
- [Simulator-based explanation and debugging of hazard-triggering events in DNN-based safety-critical systems](https://mlsanity.com/paper/2204.00480/) (2022)
- [Repairing Deep Neural Networks Based on Behavior Imitation](https://mlsanity.com/paper/2305.03365/) (2023)
- [CertPri: Certifiable Prioritization for Deep Neural Networks via Movement Cost in Feature Space](https://mlsanity.com/paper/2307.09375/) (2023)
- [FetaFix: Automatic Fault Localization and Repair of Deep Learning Model Conversions](https://mlsanity.com/paper/2312.15101/) (2023)
- [Debugging and Runtime Analysis of Neural Networks with VLMs (A Case Study)](https://mlsanity.com/paper/2503.17416/) (2025)
- [Fault Localization for Buggy Deep Learning Framework Conversions in Image Recognition](https://mlsanity.com/paper/2306.06157/) (2023)
- [Feature-Guided Analysis of Neural Networks: A Replication Study](https://mlsanity.com/paper/2511.00052/) (2025)
- [Repairing Brain-Computer Interfaces with Fault-Based Data Acquisition](https://mlsanity.com/paper/2203.10677/) (2022)
- [Toward Debugging Deep Reinforcement Learning Programs with RLExplorer](https://mlsanity.com/paper/2410.04322/) (2024)
- [Efficient Adversarial Input Generation via Neural Net Patching](https://mlsanity.com/paper/2211.16808/) (2022)
- [QNNRepair: Quantized Neural Network Repair](https://mlsanity.com/paper/2306.13793/) (2023)
- [Path Analysis for Effective Fault Localization in Deep Neural Networks](https://mlsanity.com/paper/2310.18987/) (2023)
- [DeepKnowledge: Generalisation-Driven Deep Learning Testing](https://mlsanity.com/paper/2403.16768/) (2024)
- [Robust Black-box Testing of Deep Neural Networks using Co-Domain Coverage](https://mlsanity.com/paper/2408.06766/) (2024)
- [Towards Assessing Deep Learning Test Input Generators](https://mlsanity.com/paper/2504.02329/) (2025)
- [Search-Based Software Engineering and AI Foundation Models: Current Landscape and Future Roadmap](https://mlsanity.com/paper/2505.19625/) (2025)
- [BTTackler: A Diagnosis-based Framework for Efficient Deep Learning Hyperparameter Optimization](https://mlsanity.com/paper/2602.23630/) (2026)
- [Hierarchical Fault Detection and Diagnosis for Transformer Architectures](https://mlsanity.com/paper/2604.28118/) (2026)
- [Mind the Gaps: From Failure Attribution to Closed-Form Repair of Code Language Models](https://mlsanity.com/paper/2610.05277/) (2026)

## Abstract (arXiv)

Deep Neural Networks (DNNs) are increasingly deployed in safety-critical applications including autonomous vehicles and medical diagnostics. To reduce the residual risk for unexpected DNN behaviour and provide evidence for their trustworthy operation, DNNs should be thoroughly tested. The DeepFault whitebox DNN testing approach presented in our paper addresses this challenge by employing suspiciousness measures inspired by fault localization to establish the hit spectrum of neurons and identify suspicious neurons whose weights have not been calibrated correctly and thus are considered responsible for inadequate DNN performance. DeepFault also uses a suspiciousness-guided algorithm to synthesize new inputs, from correctly classified inputs, that increase the activation values of suspicious neurons. Our empirical evaluation on several DNN instances trained on MNIST and CIFAR-10 datasets shows that DeepFault is effective in identifying suspicious neurons. Also, the inputs synthesized by DeepFault closely resemble the original inputs, exercise the identified suspicious neurons and are highly adversarial.