Why Large-Scale Code Migrations Are Challenging
Code migrations are rarely straightforward. They involve moving an existing codebase- sometimes millions of lines of code into a new framework, language or system while ensuring everything still works as intended. Several factors make this process inherently complex such as:
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Maintaining Code Quality: Code migration involves more than just syntax changes—it requires a deep understanding of business logic, dependencies, and best practices. Without careful management, migrations can introduce performance bottlenecks, security vulnerabilities, and inefficiencies.
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Ensuring Compatibility: One of the biggest challenges is ensuring that migrated code functions correctly in the new environment. Automated testing, regression analysis, and rigorous validation processes are required to maintain compatibility while preventing critical failures.
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Manual Updates: Traditional code migration efforts are highly manual and time-intensive. Engineers must analyse, refactor, and test thousands of files, making large-scale migrations prohibitively slow. Even with automation scripts, edge cases and complex logic often require manual intervention, extending migration timelines.
How LLMs Solve These Challenges
Here’s how LLMs can address common large-scale code migration challenges:
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Automating Repetitive Refactoring Tasks: LLMs can handle bulk code modifications, such as updating function signatures, modifying API calls and restructuring legacy patterns, reducing manual effort.
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Context and Intent in Code Updates: Unlike traditional search-and-replace tools, LLMs can comprehend the broader context of a codebase. They ensure that updates align with coding standards, architectural guidelines and functional requirements.
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Consistency Across Thousands of Files: LLMs can enforce consistency across a massive codebase. This prevents discrepancies that could lead to bugs or maintenance headaches down the line.
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Integrating LLM-Powered Tools into Existing Workflows: LLM-based solutions can be integrated into CI/CD pipelines, version control systems and developer IDEs, ensuring seamless adoption. Companies can leverage these tools alongside existing software development practices to streamline large-scale migrations.
How Airbnb Reduced Code Migration to Weeks with LLMs
Faced with a massive migration project, Airbnb turned to LLMs to achieve what once seemed impossible: completing code migration in weeks which would have taken years. Here’s how they did it:
The Problem: A Large-Scale Migration with Thousands of Test Files
Airbnb had to migrate nearly 3,500 React component test files from Enzyme to React Testing Library (RTL). Enzyme, once the go-to testing framework for Airbnb’s React components had become outdated and no longer aligned with modern React best practices. However, transitioning to RTL wasn’t straightforward, there were fundamental differences between the two frameworks, making an automated, one-to-one migration impossible.
Manually refactoring each test file was expected to take 1.5 years of engineering time, requiring developers to update thousands of lines of code while ensuring no loss in test coverage. Given the scale and complexity of the migration, Airbnb needed an approach that could drastically reduce the time required while maintaining code quality.
The Traditional Approach: A Lengthy and Labour-Intensive Process
Had Airbnb relied solely on manual migration, engineers would have needed to:
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Manually rewrite each Enzyme test in RTL syntax.
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Verify correctness through repeated test runs and debugging.
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Ensure test coverage parity to prevent regressions.
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Manually handle edge cases, refactoring complex test setups individually.
This slow, error-prone process would have required hundreds of developer hours per week, infeasible at Airbnb’s scale.
The LLM-Powered Approach: Completing the Migration in Just Six Weeks
Airbnb instead used LLMs with automation to complete the migration in just six weeks. Here’s how they achieved it:
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Automated Pipeline: Airbnb used an LLM-powered step-based pipeline to validate and migrate files efficiently, enabling large-scale parallel processing.
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Retry Loops: Failed migrations triggered automated retries with improved prompts, resolving most issues within 10 attempts.
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Expanded Context: LLMs received detailed inputs, including source code and test examples, improving accuracy for complex test refactorings.
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Iterative Improvement: Airbnb identified failure patterns, refined prompts and re-ran migrations. This process pushed the success rate from 75% to 97% in just four days, leaving only 100 remaining files for manual intervention.
Results and Impact
Airbnb successfully completed the entire migration in six weeks, compared to the original estimate of 1.5 years.
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75% of files migrated in 4 hours using the automated pipeline.
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97% of files completed after four days of iterative improvement.
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Remaining 3% were manually reviewed but LLM-generated baselines reduced manual effort.
Scalable Infrastructure in LLM-Driven Code Migration
To accelerate such large-scale code migrations with LLMs, you need a scalable infrastructure capable of handling massive codebases with speed and precision. Here’s how our AI Supercloud can support large-scale LLM workloads:
Compute Power: High-Performance GPUs
LLMs are computationally intensive, especially when transforming thousands of files simultaneously. We offer scalable high-performance GPU clusters like the NVIDIA HGX H100 and NVIDIA HGX H200 with massive compute power. These systems deliver the parallel processing power needed to run multiple LLM instances, accelerating inference and generation.
Storage and Data Management: Efficient Processing of Massive Codebases
Code migrations involve ingesting and analysing enormous datasets- sometimes terabytes of source files. Traditional storage systems can become bottlenecks, slowing down the process. Our AI Supercloud offers NVIDIA-certified WEKA storage with GPUDirect Storage support to address this by providing high-throughput, low-latency access to data. These systems allow LLMs to read and write files at blazing speeds, ensuring that compute resources aren’t left idle waiting for I/O.
Networking: Parallel Processing at Scale
Distributed computing environments, powered by technologies like NVLink and NVIDIA Quantum-2 InfiniBand, enable parallel processing across multiple nodes. In a cloud-based setup, thousands of files can be split across a cluster, with each node running its own LLM instance. Our high-performance networking ensures rapid data transfer between nodes, minimising latency and maximising throughput.
Explore Related Resources
FAQs
How do LLMs improve the efficiency of code migration?
LLMs automate repetitive tasks, refactor complex code structures, and ensure consistency across large codebases, significantly reducing manual intervention.
Can LLMs handle all aspects of a migration automatically?
While LLMs automate most of the process, some complex edge cases may still require manual review and refinement to ensure accuracy.
How does LLM-based migration compare to traditional manual migration?
Traditional migration is slow and labour-intensive, whereas LLM-powered approaches drastically cut time while maintaining accuracy and scalability.
How did Airbnb reduce its code migration timeline from 1.5 years to six weeks?
Airbnb used an LLM-powered automated pipeline, iterative refinement, and high-performance compute resources to process thousands of test files efficiently.
Can LLM-powered migration integrate with existing development workflows?
Yes, LLM-driven tools can be integrated into CI/CD pipelines, version control systems, and IDEs to streamline migration efforts.
October 1, 2024
Updated on 4 Apr 2025
