# MigrationBench: Repository-Level Code Migration Benchmark from Java 8

arXiv: https://arxiv.org/abs/2505.09569 · HTML: https://mlsanity.com/paper/2505.09569/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2025.

## Built on by (most cited first, 11 of 11)

- [BeyondSWE: Can Current Code Agent Survive Beyond Single-Repo Bug Fixing?](https://mlsanity.com/paper/2603.03194/) (2026)
- [LLM Agents Already Know When to Call Tools -- Even Without Reasoning](https://mlsanity.com/paper/2605.09252/) (2026)
- [SWE Refactor Bench: Can Coding Agents Complete a Long-Horizon, Whole-Repository Stack Migration?](https://mlsanity.com/paper/2608.23564/) (2026)
- [CoreCodeBench: Decoupling Code Intelligence via Fine-Grained Repository-Level Tasks](https://mlsanity.com/paper/2507.05281/) (2025)
- [BackportBench: A Multilingual Benchmark for Automated Backporting of Patches](https://mlsanity.com/paper/2512.01396/) (2025)
- [TimeMachine-bench: A Benchmark for Evaluating Model Capabilities in Repository-Level Migration Tasks](https://mlsanity.com/paper/2601.22597/) (2026)
- [SemRep: Generative Code Representation Learning with Code Transformations](https://mlsanity.com/paper/2603.13640/) (2026)
- [CLEAR: Context Augmentation from Contrastive Learning of Experience via Agentic Reflection](https://mlsanity.com/paper/2604.07487/) (2026)
- [Converted, Not Equivalent: Benchmarking Codebase Conversion via Observational Equivalence](https://mlsanity.com/paper/2605.29054/) (2026)
- [Update from Hell: Can Coding Agents Survive Hidden Breakage in Dependency Upgrades?](https://mlsanity.com/paper/2608.30300/) (2026)
- [Chronos Enables Code Agents to Reason over Software Evolution](https://mlsanity.com/paper/2610.11578/) (2026)

## Abstract (arXiv)

With the rapid advancement of powerful large language models (LLMs) in recent years, a wide range of software engineering tasks can now be addressed using LLMs, significantly enhancing productivity and scalability. Numerous benchmark datasets have been developed to evaluate the coding capabilities of these models, while they primarily focus on code generation and issue-resolution tasks. In contrast, we introduce a new coding benchmark MigrationBench with a distinct focus: code migration. MigrationBench aims to serve as a comprehensive benchmark for migration from Java 8 to the latest long-term support (LTS) versions (Java 17, 21), including a full dataset and its subset selected with 5,102 and 300 repositories respectively. selected is a representative subset curated for complexity and difficulty, offering a versatile resource to support research in the field of code migration. Additionally, we provide a comprehensive evaluation framework to facilitate rigorous and standardized assessment of LLMs on this challenging task. We further propose an agentic framework and demonstrate that LLMs can effectively tackle repository-level code migration to Java 17. For the selected subset with Claude-4.5-Sonnet, our agentic framework achieves 71.67% and 53.33% success rate (pass@1) for minimal and maximal migration respectively. The dataset and evaluation source code are available at: https://huggingface.co/collections/AmazonScience/migrationbench and https://github.com/amazon-science/MigrationBench respectively.