PYLIVE: On-the-Fly Code Change for Python-based Online Services
Haochen Huang, Chengcheng Xiang, Li Zhong, Yuanyuan Zhou
Abstract
Python is becoming a popular language for building online web services in many companies. To improve online service robustness, this paper presents a new framework, called PYLIVE, to enable on-the-fly code change. PYLIVE leverages the unique language features of Python, meta-object protocol and dynamic typing, to support dynamic logging, profiling and bug-fixing without restarting online services. PYLIVE requires no modification to the underlying runtime systems (i.e., Python interpreters), making it easy to be adopted by online services with little portability concern.
We evaluated PYLIVE with seven Python-based web applications that are widely used for online services. From these applications, we collected 20 existing real-world cases, including bugs, performance issues and patches for evaluation. PYLIVE can help resolve all the cases by providing dynamic logging, profiling and patching with little overhead. Additionally, PYLIVE also helped diagnose two new performance issues in two widely-used open-source applications.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 43d16749-9949-4a5c-9aea-e18fa4ce80b5Cited by top-tier papers2
- Effective Bug Detection with Unused DefinitionsLi Zhong, Chengcheng Xiang, Haochen Huang, Bingyu Shen et al.EuroSys 2024
- The Digital-Safety Risks of Financial Technologies for Survivors of Intimate Partner ViolenceRosanna Bellini, Kevin Lee, Megan A. Brown, Jeremy Shaffer et al.USENIX Security 2023
Builds on1
Related papers
- DynaPyt: a dynamic analysis framework for PythonAryaz Eghbali, Michael PradelFSE 2022 · 30 citations
- Noctua: Towards Automated and Practical Fine-grained Consistency AnalysisKai Ma, Cheng Li, Enzuo Zhu, Ruichuan Chen et al.EuroSys 2024 · 2 citations
- BCaLLM: Call Graph-Guided Python Breaking Change Detection with Large Language ModelsWei Cheng, Chen Shen, Huan Zhang, Yuhan Wu et al.ISSTA 2026
- The First Large-Scale Systematic Study of Python Class Pollution VulnerabilityZhengyu Liu, Jiacheng Zhong, Jianjia Yu, Muxi Lyu et al.S&P 2026
- Towards Effective Static Type-Error Detection for PythonWonseok Oh, Hakjoo OhASE 2024 · 1 citation
