Code Understanding: Techniques, Applications, and AI‑Driven Solutions

Code understanding is a crucial technology for software knowledge graphs, providing the foundation for building, testing, locating, and explaining code, and serving as the starting point for continuous integration.

The article defines code understanding as the analysis of software systems to extract internal information and workflows, highlighting static analysis, dynamic analysis, and non‑source analysis, and noting the emerging role of large language models (LLMs) in this field.

Key functions of code understanding include improving code maintainability, facilitating refactoring and optimization, detecting security vulnerabilities, generating automated tests, and enhancing team collaboration and code reuse.

A traditional code understanding pipeline consists of source code parsing, AST/IR construction, feature extraction, and generation of feature files, but faces challenges such as high expertise requirements, performance constraints, and limited extensibility.

The proposed solution introduces a three‑layer architecture: a foundational layer with multi‑language parsers, scalable storage, and caching; an analysis layer that abstracts code relationships and reduces analysis cost; and a service layer that offers open, low‑cost APIs for various downstream applications.

Typical applications at Baidu include intelligent unit testing (automatically generating test cases from code semantics), dead‑code cleaning (identifying and removing unused functions), and AI‑enhanced static analysis (AI‑SA) that leverages LLMs to detect code risks within length constraints.

In the era of large models, the article discusses the limitations of rule‑based approaches and proposes leveraging LLMs for storage, analysis, and modeling layers to improve code comprehension, risk identification, and automated documentation.

Overall, the article provides a comprehensive overview of code understanding techniques, a scalable technical solution, and real‑world AI‑driven use cases that enhance software quality and development efficiency.