ASSERTIFY: Utilizing Large Language Models to Generate Assertions for Production Code

TL;DR

This paper introduces Assertify, a tool using large language models and prompt engineering to automatically generate production assertions in code, aiming to improve debugging and documentation.

Contribution

It presents a novel end-to-end approach leveraging LLMs and few-shot learning to generate production assertions, filling a gap in existing assertion generation research.

Findings

Achieved an average ROUGE-L score of 0.526 in assertion generation

Demonstrated effectiveness of LLMs in producing developer-like assertions

Compiled a dataset of 2,810 methods from 22 Java repositories

Abstract

Production assertions are statements embedded in the code to help developers validate their assumptions about the code. They assist developers in debugging, provide valuable documentation, and enhance code comprehension. Current research in this area primarily focuses on assertion generation for unit tests using techniques, such as static analysis and deep learning. While these techniques have shown promise, they fall short when it comes to generating production assertions, which serve a different purpose. This preprint addresses the gap by introducing Assertify, an automated end-to-end tool that leverages Large Language Models (LLMs) and prompt engineering with few-shot learning to generate production assertions. By creating context-rich prompts, the tool emulates the approach developers take when creating production assertions for their code. To evaluate our approach, we compiled a dataset of 2,810 methods by scraping 22 mature Java repositories from GitHub. Our experiments demonstrate the effectiveness of few-shot learning by producing assertions with an average ROUGE-L score of 0.526, indicating reasonably high structural similarity with the assertions written by developers. This research demonstrates the potential of LLMs in automating the generation of production assertions that resemble the original assertions.