Research on aspect-oriented techniques in Python: a framework leveraging syntax tree analysis

AOP improves modularity and maintainability by centrally managing cross-cutting concerns from the business logic, making development simpler and more efficient. Despite Python’s growing popularity as a programming language in recent years, the research field of AOP in Python is limited. Aspectlib is one of the most widely used tools in Python for handling cross-cutting concern problems. However, compared to AspectJ, Aspectlib lacks detailed Pointcut definitions, leaving room for improvement in handling cross-cutting concern problems in complex scenarios. On the other hand, AST is a structured representation of code used as an intermediate form by compilers or interpreters. It captures the syntax structure of a program and supports syntax analysis, error detection, and static analysis. In this research, we develop an AOP tool specifically designed for Python, which transforms code into an AST and traverses its nodes to perform a detailed analysis of the code structure. Additionally, we define Pointcut and Advice with precise syntax specifications and implement them using Python decorators, enhancing the interception and management of cross-cutting concerns. Finally, we conduct an experiment to verify the feasibility of the proposed approach.