TY - GEN
T1 - A Study of PyTorch Bug Patterns and Memory-Related Challenges
AU - Yu, Brian
AU - Rongon, Rubayet Rahman
AU - Cao, Chen
AU - Zhang, Xuechen
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - This study presents an in-depth manual analysis of memory-related bugs within the PyTorch deep learning framework, leveraging a filtered dataset of 1,678 closed issues from the official PyTorch GitHub repository. The selected issues span a three-year period from January 1, 2020, to March 23, 2023, allowing for a comprehensive examination of trends, patterns, and solutions. This study aims to understand the correlations between the characteristics of PyTorch bugs and also the composition of the root causes behind memory bugs. The findings reveal that Correctness and Runtime Error bugs occur most frequently, with a lack of a correlation between Affected Components and Bug Symptoms. Our results highlight the need for more integrated inter-component debugging tools. Furthermore, the findings show that indexing errors occur most frequently among memory bugs. We determine that, to address the severe impact of such memory bugs, there exists a need for more comprehensive and redundant test cases. Through this analysis, this work aims to provide actionable insights for developers to improve the robustness of PyTorch, improving its reliability in machine learning applications.
AB - This study presents an in-depth manual analysis of memory-related bugs within the PyTorch deep learning framework, leveraging a filtered dataset of 1,678 closed issues from the official PyTorch GitHub repository. The selected issues span a three-year period from January 1, 2020, to March 23, 2023, allowing for a comprehensive examination of trends, patterns, and solutions. This study aims to understand the correlations between the characteristics of PyTorch bugs and also the composition of the root causes behind memory bugs. The findings reveal that Correctness and Runtime Error bugs occur most frequently, with a lack of a correlation between Affected Components and Bug Symptoms. Our results highlight the need for more integrated inter-component debugging tools. Furthermore, the findings show that indexing errors occur most frequently among memory bugs. We determine that, to address the severe impact of such memory bugs, there exists a need for more comprehensive and redundant test cases. Through this analysis, this work aims to provide actionable insights for developers to improve the robustness of PyTorch, improving its reliability in machine learning applications.
UR - https://www.scopus.com/pages/publications/85218063496
UR - https://www.scopus.com/pages/publications/85218063496#tab=citedBy
U2 - 10.1109/BigData62323.2024.10824945
DO - 10.1109/BigData62323.2024.10824945
M3 - Conference contribution
AN - SCOPUS:85218063496
T3 - Proceedings - 2024 IEEE International Conference on Big Data, BigData 2024
SP - 7586
EP - 7591
BT - Proceedings - 2024 IEEE International Conference on Big Data, BigData 2024
A2 - Ding, Wei
A2 - Lu, Chang-Tien
A2 - Wang, Fusheng
A2 - Di, Liping
A2 - Wu, Kesheng
A2 - Huan, Jun
A2 - Nambiar, Raghu
A2 - Li, Jundong
A2 - Ilievski, Filip
A2 - Baeza-Yates, Ricardo
A2 - Hu, Xiaohua
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 IEEE International Conference on Big Data, BigData 2024
Y2 - 15 December 2024 through 18 December 2024
ER -