# Silent Bugs in Deep Learning Frameworks: An Empirical Study of Keras and TensorFlow

arXiv: https://arxiv.org/abs/2112.13314 · HTML: https://mlsanity.com/paper/2112.13314/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 62 references that are in the map.
- First posted on arXiv 26 Dec 2021.

## Builds on (most cited first, 4 of 4)

- [Machine Learning Testing: Survey, Landscapes and Horizons](https://mlsanity.com/paper/1906.10742/) (2019)
- [Taxonomy of Real Faults in Deep Learning Systems](https://mlsanity.com/paper/1910.11015/) (2019)
- [A Comprehensive Study on Deep Learning Bug Characteristics](https://mlsanity.com/paper/1906.01388/) (2019)
- [Faults in Deep Reinforcement Learning Programs: A Taxonomy and A Detection Approach](https://mlsanity.com/paper/2101.00135/) (2021)

## Built on by (most cited first, 25 of 25)

- [Bugs in Large Language Models Generated Code: An Empirical Study](https://mlsanity.com/paper/2403.08937/) (2024)
- [Impacts of floating-point non-associativity on reproducibility for HPC and deep learning applications](https://mlsanity.com/paper/2408.05148/) (2024)
- [Bugs in Machine Learning-based Systems: A Faultload Benchmark](https://mlsanity.com/paper/2206.12311/) (2022)
- [LLM-Powered Test Case Generation for Detecting Bugs in Plausible Programs](https://mlsanity.com/paper/2404.10304/) (2024)
- [Bug Characterization in Machine Learning-based Systems](https://mlsanity.com/paper/2307.14512/) (2023)
- [A Survey on Failure Analysis and Fault Injection in AI Systems](https://mlsanity.com/paper/2407.00125/) (2024)
- [Towards Enhancing the Reproducibility of Deep Learning Bugs: An Empirical Study](https://mlsanity.com/paper/2401.03069/) (2024)
- [Deep Learning Library Testing: Definition, Methods and Challenges](https://mlsanity.com/paper/2404.17871/) (2024)
- [Enhancing Differential Testing With LLMs For Testing Deep Learning Libraries](https://mlsanity.com/paper/2406.07944/) (2024)
- [COMET: Coverage-guided Model Generation For Deep Learning Library Testing](https://mlsanity.com/paper/2208.01508/) (2022)
- [To Build Our Future, We Must Know Our Past: Contextualizing Paradigm Shifts in Natural Language Processing](https://mlsanity.com/paper/2310.07715/) (2023)
- [Reliability Assurance for Deep Neural Network Architectures Against Numerical Defects](https://mlsanity.com/paper/2302.06086/) (2023)
- [Understanding Agent-Reactive Bugs at the Model-Harness Boundary: An Empirical Study of LLM Agent Issue Reports](https://mlsanity.com/paper/2607.15684/) (2026)
- [Challenges in Migrating Imperative Deep Learning Programs to Graph Execution: An Empirical Study](https://mlsanity.com/paper/2201.09953/) (2022)
- [Unraveling Code Clone Dynamics in Deep Learning Frameworks](https://mlsanity.com/paper/2404.17046/) (2024)
- [An Empirical Study on Bugs Inside PyTorch: A Replication Study](https://mlsanity.com/paper/2307.13777/) (2023)
- [PyResBugs: A Dataset of Residual Python Bugs for Natural Language-Driven Fault Injection](https://mlsanity.com/paper/2505.05777/) (2025)
- [TTrace: Lightweight Error Checking and Diagnosis for Distributed Training](https://mlsanity.com/paper/2506.09280/) (2025)
- [Why Attention Fails: A Taxonomy of Faults in Attention-Based Neural Networks](https://mlsanity.com/paper/2508.04925/) (2025)
- [Imitation Game: Reproducing Deep Learning Bugs Leveraging an Intelligent Agent](https://mlsanity.com/paper/2512.14990/) (2025)
- [Comparative analysis of real bugs in open-source Machine Learning projects -- A Registered Report](https://mlsanity.com/paper/2209.09932/) (2022)
- [Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks](https://mlsanity.com/paper/2509.10694/) (2025)
- [Demystifying the Silence of Correctness Bugs in PyTorch Compiler](https://mlsanity.com/paper/2604.08720/) (2026)
- [Relaxed activation analysis of dataflow networks - A clock calculus for machine learning and real-time scheduling](https://mlsanity.com/paper/2607.21797/) (2026)
- [Silent Failures Beyond the 32-Bit Index Range: A Differential Characterization of Large-Tensor Matrix Multiplication in PyTorch's MPS Backend](https://mlsanity.com/paper/2609.22991/) (2026)

## Abstract (arXiv)

Deep Learning (DL) frameworks are now widely used, simplifying the creation of complex models as well as their integration to various applications even to non DL experts. However, like any other programs, they are prone to bugs. This paper deals with the subcategory of bugs named silent bugs: they lead to wrong behavior but they do not cause system crashes or hangs, nor show an error message to the user. Such bugs are even more dangerous in DL applications and frameworks due to the "black-box" and stochastic nature of the systems (the end user can not understand how the model makes decisions). This paper presents the first empirical study of Keras and TensorFlow silent bugs, and their impact on users' programs. We extracted closed issues related to Keras from the TensorFlow GitHub repository. Out of the 1,168 issues that we gathered, 77 were reproducible silent bugs affecting users' programs. We categorized the bugs based on the effects on the users' programs and the components where the issues occurred, using information from the issue reports. We then derived a threat level for each of the issues, based on the impact they had on the users' programs. To assess the relevance of identified categories and the impact scale, we conducted an online survey with 103 DL developers. The participants generally agreed with the significant impact of silent bugs in DL libraries and acknowledged our findings (i.e., categories of silent bugs and the proposed impact scale). Finally, leveraging our analysis, we provide a set of guidelines to facilitate safeguarding against such bugs in DL frameworks.