# Demystifying Dependency Bugs in Deep Learning Stack

arXiv: https://arxiv.org/abs/2207.10347 · HTML: https://mlsanity.com/paper/2207.10347/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2022.

## Built on by (most cited first, 9 of 9)

- [A Survey on Failure Analysis and Fault Injection in AI Systems](https://mlsanity.com/paper/2407.00125/) (2024)
- [Deep Learning Library Testing: Definition, Methods and Challenges](https://mlsanity.com/paper/2404.17871/) (2024)
- [The Last Dependency Crusade: Solving Python Dependency Conflicts with LLMs](https://mlsanity.com/paper/2501.16191/) (2025)
- [Why Does the LLM Stop Computing: An Empirical Study of User-Reported Failures in Open-Source LLMs](https://mlsanity.com/paper/2601.13655/) (2026)
- [NLP Workbench: Efficient and Extensible Integration of State-of-the-art Text Mining Tools](https://mlsanity.com/paper/2303.01410/) (2023)
- [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)
- [Understanding the Complexity and Its Impact on Testing in ML-Enabled Systems](https://mlsanity.com/paper/2301.03837/) (2023)
- [Understanding Bugs in Multi-Language Deep Learning Frameworks](https://mlsanity.com/paper/2303.02695/) (2023)

## Abstract (arXiv)

Deep learning (DL) applications, built upon a heterogeneous and complex DL stack (e.g., Nvidia GPU, Linux, CUDA driver, Python runtime, and TensorFlow), are subject to software and hardware dependencies across the DL stack. One challenge in dependency management across the entire engineering lifecycle is posed by the asynchronous and radical evolution and the complex version constraints among dependencies. Developers may introduce dependency bugs (DBs) in selecting, using and maintaining dependencies. However, the characteristics of DBs in DL stack is still under-investigated, hindering practical solutions to dependency management in DL stack. To bridge this gap, this paper presents the first comprehensive study to characterize symptoms, root causes and fix patterns of DBs across the whole DL stack with 446 DBs collected from StackOverflow posts and GitHub issues. For each DB, we first investigate the symptom as well as the lifecycle stage and dependency where the symptom is exposed. Then, we analyze the root cause as well as the lifecycle stage and dependency where the root cause is introduced. Finally, we explore the fix pattern and the knowledge sources that are used to fix it. Our findings from this study shed light on practical implications on dependency management.