Low-Resources Project-Specific Code Summarization

TL;DR

This paper introduces a meta transfer learning approach for generating high-quality code summaries tailored to specific projects with limited data, addressing a practical gap in existing large-scale models.

Contribution

It proposes a novel meta transfer learning method with lightweight fine-tuning for low-resource project-specific code summarization, improving effectiveness over existing methods.

Findings

Our method outperforms alternatives on nine real-world projects.

Project-specific knowledge can be effectively learned with limited samples.

The approach enhances code summarization quality in low-resource scenarios.

Abstract

Code summarization generates brief natural language descriptions of source code pieces, which can assist developers in understanding code and reduce documentation workload. Recent neural models on code summarization are trained and evaluated on large-scale multi-project datasets consisting of independent code-summary pairs. Despite the technical advances, their effectiveness on a specific project is rarely explored. In practical scenarios, however, developers are more concerned with generating high-quality summaries for their working projects. And these projects may not maintain sufficient documentation, hence having few historical code-summary pairs. To this end, we investigate low-resource project-specific code summarization, a novel task more consistent with the developers' requirements. To better characterize project-specific knowledge with limited training samples, we propose a meta transfer learning method by incorporating a lightweight fine-tuning mechanism into a meta-learning framework. Experimental results on nine real-world projects verify the superiority of our method over alternative ones and reveal how the project-specific knowledge is learned.