ACE: Automated Technical Debt Remediation with Validated Large Language Model Refactorings

TL;DR

This paper presents ACE, a tool leveraging validated large language models to automate code refactoring, aiming to reduce technical debt and improve code understandability efficiently.

Contribution

It introduces a data-driven approach for automated, validated code refactoring using LLMs, addressing the challenge of technical debt in software development.

Findings

ACE provides reliable refactoring suggestions.

AI-enabled refactoring mitigates technical debt.

User feedback indicates improved code understandability.

Abstract

The remarkable advances in AI and Large Language Models (LLMs) have enabled machines to write code, accelerating the growth of software systems. However, the bottleneck in software development is not writing code but understanding it; program understanding is the dominant activity, consuming approximately 70% of developers' time. This implies that improving existing code to make it easier to understand has a high payoff and - in the age of AI-assisted coding - is an essential activity to ensure that a limited pool of developers can keep up with ever-growing codebases. This paper introduces Augmented Code Engineering (ACE), a tool that automates code improvements using validated LLM output. Developed through a data-driven approach, ACE provides reliable refactoring suggestions by considering both objective code quality improvements and program correctness. Early feedback from users suggests that AI-enabled refactoring helps mitigate code-level technical debt that otherwise rarely gets acted upon.