While AI planners are widely used, there is a significant lack of detailed documentation concerning their architecture and functionality. In this bachelor thesis, we conduct an examination of the codebases of seven distinct AI planners using Static Code Analysis, addressing an area not previously explored in academic research. Our exploration uncovered various planning functionalities integral for future architectural improvements and designs. It was observed that the prevailing architecture in most AI planners is tightly-coupled, which presents challenges in maintaining and deploying planning systems. Our research suggests a transition to a modular design for enhanced maintainability and reusability of AI planner architectures and underscores the need for more research including the use of Static Code Analysis as one of the approaches.