Optimizing Large Language Models for OpenAPI Code Completion

TL;DR

This paper evaluates and improves OpenAPI code completion in large language models by fine-tuning open-source models and proposing a new benchmark, achieving significant accuracy gains over commercial tools with fewer parameters.

Contribution

It introduces task-specific optimizations for open-source LLMs, a new semantics-aware OpenAPI benchmark, and enhancements to code infilling techniques for better performance with smaller context sizes.

Findings

Fine-tuned Code Llama improves correctness by 55.2% over GitHub Copilot.

The fine-tuned model uses 25 times fewer parameters than Codex.

Proposed enhancements address context size underperformance.

Abstract

Recent advancements in Large Language Models (LLMs) and their utilization in code generation tasks have significantly reshaped the field of software development. Despite the remarkable efficacy of code completion solutions in mainstream programming languages, their performance lags when applied to less ubiquitous formats such as OpenAPI definitions. This study evaluates the OpenAPI completion performance of GitHub Copilot, a prevalent commercial code completion tool, and proposes a set of task-specific optimizations leveraging Meta's open-source model Code Llama. A semantics-aware OpenAPI completion benchmark proposed in this research is used to perform a series of experiments through which the impact of various prompt-engineering and fine-tuning techniques on the Code Llama model's performance is analyzed. The fine-tuned Code Llama model reaches a peak correctness improvement of 55.2% over GitHub Copilot despite utilizing 25 times fewer parameters than the commercial solution's underlying Codex model. Additionally, this research proposes an enhancement to a widely used code infilling training technique, addressing the issue of underperformance when the model is prompted with context sizes smaller than those used during training. The dataset, the benchmark, and the model fine-tuning code are made publicly available.