Autonomous Legacy Web Application Upgrades Using a Multi-Agent System (2501.19204v1)

Abstract: The use of 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 Many outdated web applications pose security and reliability challenges, yet companies continue using them due to the complexity and cost of upgrades.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 Additionally, findings highlight LLMs' ability to update small outdated files with high precision, even with basic prompts.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.

• The paper introduces a multi-agent system that leverages LLMs to autonomously upgrade legacy web apps.
• It employs Zero-Shot and One-Shot Learning prompts to efficiently refactor code and minimize errors.
• Performance evaluations and GitHub-released data demonstrate enhanced context retention and operational efficiency.

Overview of Autonomous Legacy Web Application Upgrades Using a Multi-Agent System

The paper entitled "Autonomous Legacy Web Application Upgrades Using a Multi-Agent System" addresses the pervasive issue of outdated web applications, which frequently pose challenges related to security, functionality, and business dependability. With the integration of LLMs and a multi-agent framework, this paper proposes an innovative approach to autonomously update legacy web applications, thereby reducing the burden of manual upgrades that are often resource-intensive and technically demanding.

Key Contributions

The principal contribution of this paper is a novel multi-agent system designed to perform autonomous code upgrades. This system leverages LLMs to facilitate the transition of legacy web applications to newer platforms, thereby enhancing security and compatibility without extensive manual intervention. The following aspects are critical highlights of the methodology and results:

1. Multi-Agent System Design: The proposed multi-agent architecture delineates roles such as task distribution, verification, and finalization across various agents to automate the updating process. This task-oriented distribution ensures minimal resource consumption while maintaining context continuity, a noted challenge in complex AI-driven processes.
2. Evaluation Methodology: To validate the system, the authors implemented Zero-Shot Learning (ZSL) and One-Shot Learning (OSL) prompts. These prompts guide the LLMs in refactoring code, addressing deprecated components, and modernizing web frameworks. The accuracy of updates was measured by the occurrence and categorization of errors in processed files, and through the fulfiLLMent of complex task requirements.
3. Performance Metrics: The effectiveness of the system was determined by the reduced number of errors in the upgrade outcomes. Comparisons were drawn between the outputs of the standalone LLM and the multi-agent system, where the latter demonstrated superior context retention and solution generation in multiple test cases.
4. Release of Evaluation Results: The data collected from this paper is made available on GitHub, which serves as a valuable resource for validating the proposed system and facilitating further research.

Implications and Future Directions

The implications of autonomous code refactoring through LLMs extend beyond expedited updates of legacy systems, potentially impacting the entire software lifecycle management. This transformative capability could lead to significant operational efficiencies for enterprises dependent on aging technologies.

Theoretically, this approach offers a robust framework for future studies focusing on AI-driven systems for maintaining software, given the continual advancements in LLM capabilities. As these models become more adept at understanding and transforming complex legacy codes, they present an opportunity to further automate numerous facets of software engineering.

Future research could explore extending this multi-agent paradigm to broader applications, such as full-stack upgrades and cross-language code translation, leveraging improvements in LLMs and their scaling capabilities. Additionally, more granular assessments and optimizations can target specific challenges within the updating process, such as testing robustness and reducing error introduction due to model hallucinations.

Conclusion

This paper provides a methodological foundation for automated upgrades of legacy web applications, demonstrating the potential of multi-agent systems paired with LLMs in streamlining such processes. As AI technology evolves, increasing efficiency and reducing the costs associated with tech debt are realistically within reach, suggesting a paradigm shift in legacy systems management. The paper's findings serve as a stepping stone towards fully autonomous software maintenance and evolution, which is a compelling prospect for both academia and industry.