Project-Specific Code Summarization with Meta-Learning and Explainability Techniques

Code summarization generates natural language descriptions for code snippets, enhancing readability and maintainability.While current methods perform well with large-scale datasets, they struggle in low-resource scenarios typical of smaller and newer projects.Additionally, developers need summaries that capture project-specific characteristics rather than generic descriptions.To address these challenges, we propose a meta-learning-based training framework that adapts the model to individual projects as distinct tasks, even with minimal data.We introduce a strategy for selecting support projects to boost the framework's effectiveness.Experiments on eight real-world projects show that our method outperforms the baseline approach.Furthermore, we use explainability techniques to clarify the prediction process and identify potential issues.