Mono2Sls: Automated Monolith-to-Serverless Migration via Multi-Stage Pipeline with Static Analysis

2026-04-27

AI Summary

This work addresses the challenge of migrating monolithic backends to serverless architectures, a process typically requiring extensive manual effort. The authors propose the first automated migration pipeline that integrates lightweight static analysis with multi-agent collaboration. By constructing call graphs and identifying asynchronous behaviors, the approach orchestrates four specialized LLM agents—Architect, Developer, SAM Engineer, and Consistency Validator—to jointly generate end-to-end deployable applications compliant with AWS SAM specifications. Evaluated on six real-world benchmarks comprising over 10,000 lines of code and 76 endpoints, the method achieves a 100% deployment success rate, 66.1% end-to-end correctness, and a 98.7% F1 score for API coverage, substantially outperforming existing commercial solutions.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Cloud computing platforms offer elastic scaling, managed infrastructure, and pay-per-use pricing, but moving existing monolithic backends to them remains a difficult software engineering task. In practice, the migration requires coordinated changes to program structure, source code, infrastructure configuration, and cloud-specific design decisions, and these changes are still largely carried out by hand. In this paper, we present Mono2Sls, an automated pipeline that converts monolithic web backends into deployable AWS SAM applications. The pipeline combines lightweight static analysis of entry points, call graphs, and asynchronous behavior with four sequential tool-using LLM agents: Architect, Code Developer, SAM Engineer, and Consistency Validator. These agents communicate through explicit intermediate artifacts and consult a curated SAM knowledge base. Evaluated on six benchmark applications totaling more than 10K lines of code and 76 business endpoints, Mono2Sls achieves 100% deployment success without manual fixes. It also reaches 66.1% end-to-end correctness and 98.7% API-coverage F1, whereas the commercial baselines achieve 53.7--61.2% and 88.4%, respectively. The migrated systems show more consistent use of AWS-native authentication and asynchronous patterns, and an ablation study indicates that static-analysis-guided architecture planning contributes 23.4 percentage points to end-to-end correctness.

Problem

• monolith-to-serverless migration
• cloud computing
• automated software migration
• serverless architecture
• AWS SAM

Innovation

• automated migration
• static analysis
• LLM agents
• serverless computing
• AWS SAM