Beyond Decomposition: A LLM-Powered Automated Approach to Refactoring Monoliths Into Microservices
Bibliographic record
Abstract
Organizations migrating monolithic applications to microservice architectures often face significant challenges in both decomposition and refactoring phases. While the decomposition step has received considerable automation research, refactoring remains predominantly manual, creating bottlenecks in migration efforts and preventing runtime-based and a more realistic evaluation of decomposition techniques. We propose a fully automated refactoring methodology that complements existing decomposition approaches. Our technique implements an ID-based and DTO-based hybrid design for inter-service communication and leverages Large Language Models (LLMs) for decision making, code analysis and code generation. Taking a monolith's source code and decomposition plan as input, our approach identifies “API classes” that cross service boundaries, selects their appropriate target design among the ID and DTO based methods and then automatically generates the necessary communication components—API contracts, server-side endpoints, and client-side proxies. This approach balances the preservation of the monolith's workflow consistency through the ID-based design and minimizing the overhead and complexity of the cross-service interactions through the DTO-based design. A qualitative evaluation using three benchmark applications demonstrates our approach's feasibility and advantages over related work.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".