Towards Explainable Code Readability Classification With Graph Neural Networks

Abstract

Code readability is of central concern for developers, as a more readable code indicates higher maintainability, reusability, and portability. In recent years, many deep learning–based code readability classification methods have been proposed. Among them, a graph neural network (GNN)–based model has achieved the best performance in the field of code readability classification. However, it is still unclear what aspects of the model's input lead to its decisions, which hinders its practical use in the software industry. To improve the interpretability of existing code readability classification models and identify key code characteristics that drive their readability predictions, we propose an explanation framework with GNN explainers towards transparent and trustworthy code readability classification. First, we propose a simplified Abstract Syntax Tree (AST)–based code representation method, which transforms Java code snippets into ASTs and discards lower-level nodes with limited information. Then, we retrain the state-of-the-art GNN-based model together with our simplified program graphs. Finally, we employ SubgraphX to explain the model's code readability predictions at the subgraph level and visualize the explanation results to further analyze what causes such predictions. The experimental results show that sequential logic, code comments, selection logic, and nested structure are the most influential code characteristics when classifying code snippets as readable or unreadable. Further investigations indicate the model's proficiency in capturing features related to complex logic structures and extensive data flows but point to its limitations in identifying readability issues associated with naming conventions and code formatting. The explainability analysis conducted in this research is the first step towards more transparent and reliable code readability classification. We believe that our findings are useful in providing constructive suggestions for developers to write more readable code and delimitating directions for future model improvement.