Few-shot training LLMs for project-specific code-summarization (2207.04237v2)

Abstract: Very LLMs, such as GPT-3 and Codex have achieved state-of-the-art performance on several natural-language tasks, and show great promise also for code. A particularly exciting aspect of LLMs is their knack for few-shot and zero-shot learning: they can learn to perform a task with very few examples. Few-shotting has particular synergies in software engineering, where there are a lot of phenomena (identifier names, APIs, terminology, coding patterns) that are known to be highly project-specific. However, project-specific data can be quite limited, especially early in the history of a project; thus the few-shot learning capacity of LLMs might be very relevant. In this paper, we investigate the use few-shot training with the very large GPT (Generative Pre-trained Transformer) Codex model, and find evidence suggesting that one can significantly surpass state-of-the-art models for code-summarization, leveraging project-specific training.

Few-shot training LLMs for project-specific code-summarization

The paper "Few-shot training LLMs for project-specific code-summarization" investigates the capabilities of LLMs, specifically Codex, in generating code summaries through few-shot training. This work is grounded in the potential of LLMs to adapt to project-specific needs with minimal data input, a feature that is particularly useful in the domain of automated software engineering.

Overview

LLMs, such as GPT-3 and Codex, have demonstrated proficiency in executing a variety of natural language tasks and have begun to show promise in the field of code generation. These models are adept at few-shot and zero-shot learning, allowing them to perform tasks with limited data samples. The relevance of few-shot learning in software engineering is notably tied to the project-specific nature of software development, where unique identifiers, APIs, coding patterns, and terminologies are prevalent. This project-centric focus raises challenges due to the limited amount of data available, particularly in the early stages of a project's lifecycle.

This paper employs the Codex model to experiment with few-shot training approaches using the CodeXGLUE dataset, a multilingual benchmark for code summarization. The experiments are conducted across several programming languages, including Java, Python, and JavaScript, and involve comparisons with traditional fine-tuned models such as CodeBERT, GraphCodeBERT, and CodeT5.

Findings

The paper reports promising results for few-shot training with Codex, showing significant improvement over existing models:

• Cross-project Few-shot Training: Codex achieves superior performance across all languages examined, notably outperforming foundation models trained with extensive datasets. The improvements are quantified with substantial BLEU-4 score enhancements, ranging from 1.17% to 15.23%, depending on the language.
• Same-project Few-shot Training: When utilizing project-specific data, Codex's performance gains are even more pronounced, demonstrating up to 46.31% improvement over a cross-project setup. This indicates the advantage of leveraging shared vocabulary and coding patterns inherent within the same project.

The paper also discusses a statistical evaluation that confirms significant improvements using few-shot training, particularly for JavaScript and Go.

Implications

The findings underline the efficiency of few-shot learning for LLMs in software engineering contexts, reducing the need for large, cumbersome datasets and enabling rapid adaptation to new projects with minimal input. This approach presents a promising avenue for not only code summarization but potentially for other domain-specific software engineering tasks as well.

The implications extend to practical applications in software maintenance, where automated code summarization can assist in aligning comments with code changes, thereby enhancing code readability and reducing misalignment issues.

Future Directions

Future research could explore extending few-shot training capabilities to further tasks in software engineering, such as automated code generation and bug detection. Additionally, the methodology could be fine-tuned and adapted for even more granular localization, perhaps focusing on file-level or method-level adjustments. As LLMs continue to evolve, integrating their adaptive learning capabilities with domain-specific data remains a rich area for advancing automated software engineering tools.

In summary, this paper contributes valuable insights into the utility of using few-shot learning with LLMs for code summarization, proving both feasible and remarkably effective compared to traditional methods, with considerable implications for practical software development and maintenance.