mlsanityData to 7 Oct 2026

Mutation-based Fault Localization of Deep Neural Networks

arXiv 2309.05067 ↗First posted 10 Sep 2023cs.SE cs.LGcited by 5

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Abstract (arXiv)

Deep neural networks (DNNs) are susceptible to bugs, just like other types of software systems. A significant uptick in using DNN, and its applications in wide-ranging areas, including safety-critical systems, warrant extensive research on software engineering tools for improving the reliability of DNN-based systems. One such tool that has gained significant attention in the recent years is DNN fault localization. This paper revisits mutation-based fault localization in the context of DNN models and proposes a novel technique, named deepmufl, applicable to a wide range of DNN models. We have implemented deepmufl and have evaluated its effectiveness using 109 bugs obtained from StackOverflow. Our results show that deepmufl detects 53/109 of the bugs by ranking the buggy layer in top-1 position, outperforming state-of-the-art static and dynamic DNN fault localization systems that are also designed to target the class of bugs supported by deepmufl. Moreover, we observed that we can halve the fault localization time for a pre-trained model using mutation selection, yet losing only 7.55% of the bugs localized in top-1 position.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 17 of its 69 references that are in the map. First posted on arXiv 10 Sep 2023.

Builds on

17 of 69 references.

References · 17

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2017DeepXplore: Automated Whitebox Testing of Deep Learning Systemscited by 307
  3. 2018Guiding Deep Learning System Testing using Surprise Adequacycited by 123
  4. 2019Taxonomy of Real Faults in Deep Learning Systemscited by 90
  5. 2018DeepMutation: Mutation Testing of Deep Learning Systemscited by 66
  6. 2019A Comprehensive Study on Deep Learning Bug Characteristicscited by 65
  7. 2021DeepLocalize: Fault Localization for Deep Neural Networkscited by 37
  8. 2020Repairing Deep Neural Networks: Fix Patterns and Challengescited by 33
  9. 2019DeepFault: Fault Localization for Deep Neural Networkscited by 29
  10. 2018Practical Program Repair via Bytecode Mutationcited by 19
  11. 2021DeepDiagnosis: Automatically Diagnosing Faults and Recommending Actionable Fixes in Deep Learning Programscited by 19
  12. 2022DeepFD: Automated Fault Diagnosis and Localization for Deep Learning Programscited by 16
5 more
  1. 2022Bugs in Machine Learning-based Systems: A Faultload Benchmarkcited by 15
  2. 2021Automatic Fault Detection for Deep Learning Programs Using Graph Transformationscited by 9
  3. 2017Empirical Evaluation of Mutation-based Test Prioritization Techniquescited by 4
  4. 2014Inferring Loop Invariants by Mutation, Dynamic Analysis, and Static Checkingcited by 1
  5. 2016A Theoretical Framework for Understanding Mutation-Based Testing Methodscited by 1

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Cited by year

Citing papers per year: 2023: 1; 2024: 1; 2025: 3.

5 papers.

  1. 2024BLAZE: Cross-Language and Cross-Project Bug Localization via Dynamic Chunking and Hard Example Learningcited by 7
  2. 2025Debugging and Runtime Analysis of Neural Networks with VLMs (A Case Study)cited by 3
  3. 2025Feature-Guided Analysis of Neural Networks: A Replication Studycited by 2
  4. 2025On Accelerating Deep Neural Network Mutation Analysis by Neuron and Mutant Clusteringcited by 1
  5. 2023Path Analysis for Effective Fault Localization in Deep Neural Networkscited by 0