Year: 2025
Type: conference-paper
Abstract: 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.
Authors: Quang-Huy Nguyen, Hoai-Phong Le, Bac Le
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4409480625