# Understanding Bugs in Multi-Language Deep Learning Frameworks

arXiv: https://arxiv.org/abs/2303.02695 · HTML: https://mlsanity.com/paper/2303.02695/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 32 references that are in the map.
- First posted on arXiv 5 Mar 2023.

## Builds on (most cited first, 10 of 10)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems](https://mlsanity.com/paper/1512.01274/) (2015)
- [A Comprehensive Study on Deep Learning Bug Characteristics](https://mlsanity.com/paper/1906.01388/) (2019)
- [Toward Understanding Deep Learning Framework Bugs](https://mlsanity.com/paper/2203.04026/) (2022)
- [Understanding Performance Problems in Deep Learning Systems](https://mlsanity.com/paper/2112.01771/) (2021)
- [Demystifying Dependency Bugs in Deep Learning Stack](https://mlsanity.com/paper/2207.10347/) (2022)
- [On the Impact of Programming Languages on Code Quality](https://mlsanity.com/paper/1901.10220/) (2019)
- [Towards Understanding the Faults of JavaScript-Based Deep Learning Systems](https://mlsanity.com/paper/2209.04791/) (2022)
- [Are Multi-language Design Smells Fault-prone? An Empirical Study](https://mlsanity.com/paper/2010.14331/) (2020)
- [Static Code Analysis of Multilanguage Software Systems](https://mlsanity.com/paper/1906.00815/) (2019)

## Abstract (arXiv)

Deep learning frameworks (DLFs) have been playing an increasingly important role in this intelligence age since they act as a basic infrastructure for an increasingly wide range of AIbased applications. Meanwhile, as multi-programming-language (MPL) software systems, DLFs are inevitably suffering from bugs caused by the use of multiple programming languages (PLs). Hence, it is of paramount significance to understand the bugs (especially the bugs involving multiple PLs, i.e., MPL bugs) of DLFs, which can provide a foundation for preventing, detecting, and resolving bugs in the development of DLFs. To this end, we manually analyzed 1497 bugs in three MPL DLFs, namely MXNet, PyTorch, and TensorFlow. First, we classified bugs in these DLFs into 12 types (e.g., algorithm design bugs and memory bugs) according to their bug labels and characteristics. Second, we further explored the impacts of different bug types on the development of DLFs, and found that deployment bugs and memory bugs negatively impact the development of DLFs in different aspects the most. Third, we found that 28.6%, 31.4%, and 16.0% of bugs in MXNet, PyTorch, and TensorFlow are MPL bugs, respectively; the PL combination of Python and C/C++ is most used in fixing more than 92% MPL bugs in all DLFs. Finally, the code change complexity of MPL bug fixes is significantly greater than that of single-programming-language (SPL) bug fixes in all the three DLFs, while in PyTorch MPL bug fixes have longer open time and greater communication complexity than SPL bug fixes. These results provide insights for bug management in DLFs.