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arXiv · 2401.01571

Principles and Practices of Large-Scale Code Analysis at Ant Group: A Data- and Logic-Oriented Approach

Xiaoheng Xie·Gang Fan·Xiaojun Lin·Ang Zhou·Shijie Li·Xunjin Zheng·Yinan Liang·Yu Zhang·Na Yu·Haokun Li·Xinyu Chen·Yingzhuang Chen·Yi Zhen·Dejun Dong·Xianjin Fu·Jinzhou Su·Fuxiong Pan·Pengshuai Luo·Youzheng Feng·Ruoxiang Hu·Hanyang Guo·Jing Fan·Xiao Xiao·Peng Di
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Abstract

Large-scale software development requires dynamic and multifaceted static code analysis that extends beyond the capabilities of traditional tools. Existing tools like CodeQL lack cross-language analysis capabilities and can be time-consuming and resource-intensive. We present CodeFuse-Query, a data system tailored for large-scale code analysis. First, CodeFuse-Query adopts a Logic-Oriented Computation Design, employing Datalog with a two-tiered schema, COREF, to convert source code into data facts, and Godel to express complex analysis tasks in logical terms. Furthermore, CodeFuse-Query adopts a Domain-Optimized System Design. This approach optimizes resource utilization, prioritizes data reusability, applies incremental code extraction, and introduces task-type characteristics specifically for code changes, underscoring its domain-optimized design. We present empirical results demonstrating CodeFuse-Query's robustness, scalability, and efficiency in large-scale real-world scenarios at Ant Group, where it serves as a core static analysis infrastructure. Deployed in production environments, CodeFuse-Query processes up to 10 billion lines of code daily across more than 300,000 distinct analysis tasks. CodeFuse-Query has been open-sourced.

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Xiaoheng Xie, Gang Fan, Xiaojun Lin, Ang Zhou, Shijie Li, Xunjin Zheng, Yinan Liang, Yu Zhang, Na Yu, Haokun Li, Xinyu Chen, Yingzhuang Chen, Yi Zhen, Dejun Dong, Xianjin Fu, Jinzhou Su, Fuxiong Pan, Pengshuai Luo, Youzheng Feng, Ruoxiang Hu, Hanyang Guo, Jing Fan, Xiao Xiao, Peng Di. 2024-01-03. Principles and Practices of Large-Scale Code Analysis at Ant Group: A Data- and Logic-Oriented Approach. https://doi.org/10.1145/3786583.3786907

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