# Autonomous Legacy Web Application Upgrades Using a Multi-Agent System

arXiv: https://arxiv.org/abs/2501.19204 · HTML: https://mlsanity.com/paper/2501.19204/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 19 of its 43 references that are in the map.
- First posted on arXiv 31 Jan 2025.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [Refining ChatGPT-Generated Code: Characterizing and Mitigating Code Quality Issues](https://mlsanity.com/paper/2307.12596/) (2023)
- [What's Wrong with Your Code Generated by Large Language Models? An Extensive Study](https://mlsanity.com/paper/2407.06153/) (2024)
- [Towards Translating Real-World Code with LLMs: A Study of Translating to Rust](https://mlsanity.com/paper/2405.11514/) (2024)
- [CodePori: Large-Scale System for Autonomous Software Development Using Multi-Agent Technology](https://mlsanity.com/paper/2402.01411/) (2024)
- [AI-powered Code Review with LLMs: Early Results](https://mlsanity.com/paper/2404.18496/) (2024)
- [Autonomous Agents in Software Development: A Vision Paper](https://mlsanity.com/paper/2311.18440/) (2023)
- [Artificial-Intelligence Generated Code Considered Harmful: A Road Map for Secure and High-Quality Code Generation](https://mlsanity.com/paper/2409.19182/) (2024)
- [Large Language Model Evaluation Via Multi AI Agents: Preliminary results](https://mlsanity.com/paper/2404.01023/) (2024)
- [Large-scale, Independent and Comprehensive study of the power of LLMs for test case generation](https://mlsanity.com/paper/2407.00225/) (2024)
- [COBOL to Java and Newspapers Still Get Delivered](https://mlsanity.com/paper/1808.03724/) (2018)
- [TimeLess: A Vision for the Next Generation of Software Development](https://mlsanity.com/paper/2411.08507/) (2024)
- [Microservices Migration in Industry: Intentions, Strategies, and Challenges](https://mlsanity.com/paper/1906.04702/) (2019)
- [Penetration Testing and Legacy Systems](https://mlsanity.com/paper/2402.10217/) (2023)
- [Transforming C++11 Code to C++03 to Support Legacy Compilation Environments](https://mlsanity.com/paper/2405.07204/) (2024)

## Built on by (most cited first, 1 of 1)

- [DiLLS: Interactive Diagnosis of LLM-based Multi-agent Systems via Layered Summary of Agent Behaviors](https://mlsanity.com/paper/2602.05446/) (2026)

## Abstract (arXiv)

The use of Large Language Models (LLMs) for autonomous code generation is gaining attention in emerging technologies. As LLM capabilities expand, they offer new possibilities such as code refactoring, security enhancements, and legacy application upgrades. Many outdated web applications pose security and reliability challenges, yet companies continue using them due to the complexity and cost of upgrades. To address this, we propose an LLM-based multi-agent system that autonomously upgrades legacy web applications to the latest versions. The system distributes tasks across multiple phases, updating all relevant files. To evaluate its effectiveness, we employed Zero-Shot Learning (ZSL) and One-Shot Learning (OSL) prompts, applying identical instructions in both cases. The evaluation involved updating view files and measuring the number and types of errors in the output. For complex tasks, we counted the successfully met requirements. The experiments compared the proposed system with standalone LLM execution, repeated multiple times to account for stochastic behavior. Results indicate that our system maintains context across tasks and agents, improving solution quality over the base model in some cases. This study provides a foundation for future model implementations in legacy code updates. Additionally, findings highlight LLMs' ability to update small outdated files with high precision, even with basic prompts. The source code is publicly available on GitHub: https://github.com/alasalm1/Multi-agent-pipeline.