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 · 2026
Large-scale software development requires dynamic and multifaceted static code analysis that extend beyond the capabilities of traditional tools. Existing tools like CodeQL lack cross-language analysis capability and are 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 Gödel to express complex analysis tasks in logical terms. Furthermore, CodeFuse-Query adopts Domain Optimized System Design. This approach optimizes resource utilization, prioritizes data reusability, applies incremental code extraction, and introduces tasks type characteristics specially for Code Change, underscoring its domain-optimized design.