CodeFuse-Query: A Data-Centric Static Code Analysis System for Large-Scale Organizations

In the domain of large-scale software development, the demands for dynamic and multifaceted static code analysis exceed the capabilities of traditional tools. To bridge this gap, we present CodeFuse-Query, a system that redefines static code analysis through the fusion of Domain Optimized System Des...

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Bibliographic Details
Published in:arXiv.org 2024-01
Main Authors: Xie, Xiaoheng, Fan, Gang, Lin, Xiaojun, Zhou, Ang, Li, Shijie, Zheng, Xunjin, Liang, Yinan, Zhang, Yu, Yu, Na, Li, Haokun, Chen, Xinyu, Chen, Yingzhuang, Yi Zhen, Dong, Dejun, Fu, Xianjin, Su, Jinzhou, Pan, Fuxiong, Luo, Pengshuai, Feng, Youzheng, Hu, Ruoxiang, Fan, Jing, Zhou, Jinguo, Xiao, Xiao, Peng, Di
Format: Article
Language:English
Subjects:
Computation
Data analysis
Design optimization
Empirical analysis
Queries
Query languages
Resource utilization
Software
Software development
Source code
Static code analysis
Systems design
Task complexity
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Summary:In the domain of large-scale software development, the demands for dynamic and multifaceted static code analysis exceed the capabilities of traditional tools. To bridge this gap, we present CodeFuse-Query, a system that redefines static code analysis through the fusion of Domain Optimized System Design and Logic Oriented Computation Design. CodeFuse-Query reimagines code analysis as a data computation task, support scanning over 10 billion lines of code daily and more than 300 different tasks. It optimizes resource utilization, prioritizes data reusability, applies incremental code extraction, and introduces tasks types specially for Code Change, underscoring its domain-optimized design. The system's logic-oriented facet employs Datalog, utilizing a unique two-tiered schema, COREF, to convert source code into data facts. Through Godel, a distinctive language, CodeFuse-Query enables formulation of complex tasks as logical expressions, harnessing Datalog's declarative prowess. This paper provides empirical evidence of CodeFuse-Query's transformative approach, demonstrating its robustness, scalability, and efficiency. We also highlight its real-world impact and diverse applications, emphasizing its potential to reshape the landscape of static code analysis in the context of large-scale software development.Furthermore, in the spirit of collaboration and advancing the field, our project is open-sourced and the repository is available for public access
ISSN:2331-8422