Application Modernization with LLMs: Addressing Core Challenges in Reliability, Security, and Quality

TL;DR

This paper explores how large language models can be used to modernize applications, focusing on improving reliability, security, and quality through a framework that combines AI capabilities with human expertise, demonstrated via a real-world case study.

Contribution

It introduces a framework leveraging LLMs' reasoning and generation abilities, emphasizing human involvement for effective application modernization, and provides a practical case study with a reference implementation.

Findings

Framework effectively integrates LLMs and human expertise

Case study demonstrates practical application and benefits

Highlights importance of human guidance in AI-assisted modernization

Abstract

AI-assisted code generation tools have revolutionized software development, offering unprecedented efficiency and scalability. However, multiple studies have consistently highlighted challenges such as security vulnerabilities, reliability issues, and inconsistencies in the generated code. Addressing these concerns is crucial to unlocking the full potential of this transformative technology. While advancements in foundational and code-specialized language models have made notable progress in mitigating some of these issues, significant gaps remain, particularly in ensuring high-quality, trustworthy outputs. This paper builds upon existing research on leveraging large language models (LLMs) for application modernization. It explores an opinionated approach that emphasizes two core capabilities of LLMs: code reasoning and code generation. The proposed framework integrates these capabilities with human expertise to tackle application modernization challenges effectively. It highlights the indispensable role of human involvement and guidance in ensuring the success of AI-assisted processes. To demonstrate the framework's utility, this paper presents a detailed case study, walking through its application in a real-world scenario. The analysis includes a step-by-step breakdown, assessing alternative approaches where applicable. This work aims to provide actionable insights and a robust foundation for future research in AI-driven application modernization. The reference implementation created for this paper is available on GitHub.