Refactoring & Migrations with AI: Smarter Code Transformation at Scale

Hello everybody. Today I want to share how AI is fundamental to change the game for refactoring and large-scale migrations. We are not talking about simple autocomplete anymore. We are talking about AI systems that can analyze thousands of files, understand complex dependencies and automate transformations that previously took teams months, even years.

In the next 20 minutes, we'll explore how tools like GitHub, Copilot, OpenAI Codecs, and other specialized migration frameworks make these complex transformations faster, safer, and more manageable. And more importantly, I will share some real-world lessons learned from my experience and show you how to balance automation with human oversight that is still essential. So when I hear about refactoring with AI, I always think about this tweet.

None of it worked, but boy, was it beautiful. No, but seriously, the idea of AI refactoring has been around for a while, and earlier attempts were clunky and error-prone and often generating more work than they saved. But fast forward to today, and we are seeing AI tools that can handle complex tasks with impressive accuracy. So there is a way forward that we will explore today.

But first, some context. Large-scale code migrations and refactoring projects are just notoriously difficult. Legacy system modernization projects have more than 70% of failure rate. It is like more than two softs. And some examples of this are Facebook transition to React 16, architectures that took more than two years. And Google's recent research that showed that large-scale migrations traditionally require hundreds of software engineering years to complete manually. But here's what's changing. Google recently used AI-powered tooling to reduce migration time by 50%, while having an AI generate 74% of code changes across more than 500 modifications. And Reddit used AI to detect anti-patterns and refactor across 2,000 repositories efficiently. These are not just isolated success stories. They represent a fundamental shift in how we approach cloud transformation. The challenge is not just scale. It is complexity.

Modern applications have intricate dependency graphs, business logic scattered across multiple layers, and the constant pressure to maintain service availability during transitions. Traditional tools like static analyzers and charging replay script just cannot handle contextual understanding required for large and safe migrations. So here's what we will cover in the next 20 minutes. One is types of refactoring that AI can do with some examples. Two is AI-powered refactoring tools. And three, essential rules for LLM-powered refactoring.

And four, some just final thoughts and recommendations. So, let's look at how AI can be used for refactoring tasks. AI excels at three critical types of large-scale refactoring via previously maintenance methods. First is framework and language migrations. AI can handle complex syntax transformations, API migrations, and even cross-language conversions. We are seeing successful Python-to-Python seq migrations, AngularJS-to-Angular transitions, and Java version upgrades that maintain business logic integrity while adopting modern patterns. Second is architectural transformations. It is the most impressive capability, breaking monoliths into micro-services, instructing shared libraries, and restructuring for better separation of concerns. AI can analyze call graphs, identify boundaries, and suggest database decomposition strategies. Third is performance and security improvements. AI tools can identify performance bottlenecks, suggest algorithmic improvements, and even detect security vulnerabilities during factoring. They are particularly effective at replacing inefficient patterns with optimized alternatives while preserving functionality. So, what tools are available today that can help you with this complex task? Most prominent, of course, is GPT-5 Codecs. It is specifically optimized for complex fair engineering tasks. It is just not just your basic completion tool. It can operate autonomously for up to 7 hours, handling multi-file changes spanning across thousands of lines. In direct evaluation, GPT-5 Codecs achieved more than 50% of accuracy, compared to 39% for standard GPT models. And there is, of course, GitHub Copilot. It has evolved beyond individual developer assistance. Enterprise version can now include specialized refactoring capabilities that understand your entire codebase context. It can suggest architectural patterns, identify code smells across repositories, and propose systematic improvements. There are also specialized migration tools like, for example, SortGraph, Aviator agents, and TwigFunction. They are purposefully built for large-scale transformations. Aviator agents, for example, act as an intelligent migration assistant, finding references, understanding dependencies, and breaking down large transformations into manageable chunks. So, before we dive into examples, let's establish the foundational principles that separate successful AI refactoring implementations from disasters. Based on Google research and just industry-best practices, here are some six non-negotiable rules. And the first is the obvious one. It is to preserve above all the behavior. Refactored code must behave identically to the original under all conditions. This means that comprehensive regression testing, edge case validation, and performance benchmarking are required.

If you can't prove behavioral equivalence, do not deploy the change. Rule two is about context. It is key. And LLMs need extensive context to make good refactoring decisions. Never pass just a single function or class. Include related files, dependencies, test cases, and even documentation. In Google documentation, their success came from providing up to 50 related files for just one transformation. The more context you provide, the better refactoring suggestions.

Rule three is to implement smart retry mechanisms with learning. Do not expect perfect results on the first attempt. Build retry loops that learn from failures. When an LLM makes a mistake, include that error information in the next attempt. Google migration system used interactive prompting where each failure informs the next attempt, dramatically improving success rates. Rule four is to never trust. Every AI-generated refactoring must pass through, like multiple validation gates, syntactic correction, semantic correction, and business logic preservation through end-to-end and unit testing, and performance impact assessment through benchmarking. Up to 76% of initial LLM refactoring suggestions can be hallucinations, so validation is absolutely crucial.

Rule five is to start small and scale gradually. Begin with low-risk isolated components. Extract simple functions before attempting architectural transformations. This builds confidence in you and proceeds to help you identify edge cases and before tackling critical systems. And final rule is to document everything, rationale, process, and changes. AI refactoring can make changes that are not immediately obvious to human reviewers. Document why refactoring was needed, what AI changed, and how the new code improves the original. Documentation is crucial for maintenance and debugging.

Framework migrations represent one of the most challenging yet impactful refactoring scenarios where AI truly shines. Unlike simple syntax exchanges, framework migrations require understanding of architectural patterns, dependency relations, and subtle behavioral differences between old and new paradigms. AI excels at pattern recognition and can handle multiple transformation rules across thousands of files systematically. Real-world examples include AngularJS to Angular migrations, traditionally taking months but reduced to two to four months with AI assistance for higher code quality. AI capabilities encompass pattern recognition, syntax transformation, type safety introduction for seamless transitions, and tested migrations for maintaining behavior coverage.

Seamless migration examples from AngularJS to Angular showcase AI's identification of component patterns, utilization of service injection patterns, syntax updates, and modern programming model enhancements. The speaker presents a delivered legacy AngularJS repository as a small demonstration app for migration. By leveraging GPT-5 context, the presenter compiles a comprehensive prompt with mission statements, context, scope patterns, quality verification, and output requirements. Following the rule of providing extensive context, all files from the root are included, initiating the migration process with the expectation of a comprehensive result.

The speaker demonstrates the migration process from AngularJS to Angular using AI assistance, indicating the inclusion of an SRC folder and all root files for the migration. Acknowledging the time-consuming nature of the process, the speaker anticipates cutting to the point of showcasing the migration result. The process involves utilizing AI for efficient and accurate migration, emphasizing the importance of context, pattern recognition, and systematic consistency in achieving successful and high-quality framework migrations.

So seamless migration from JS to JS with proper interface and definition. And tested migration for automatic updates to test suits that maintain behavior coverage. So let me show you a real AngularJS to Angular migration. This example demonstrates how AI identifies component patterns and can use service injection patterns, update surface syntax, and enhance the actual programming model, traditionally while maintaining identical business version and adopting TypeScript and modern parsing patterns.

So here I have a delivered legacy AngularJS repository. It is quite a small app. As I like, it is obviously not a production code from anything that I can show, but it is something that I've quickly did for this presentation. And I can show how it works by running start and seeing what we have here. So it is a quite small app. So to essentially use a directory, you can activate or deactivate users, and just do whatever, and preview with their data. So let's show how using GPT-5 context, it's compiled. You can quickly and efficiently migrate it from AngularJS pattern.

Following the rule about context, let's add everything that we have here, which would be an SRC folder, and just all of the files from the root. Basically, we should just have a package and that should be more than enough. And let's start. This may take quite a while, so I will probably just cut to the point where we got the result. After less than 30 minutes, we are back. And it did quite an impressive job. It runs the whole application from AngularJS to Angular and updated and even added the tests that are passing. It got a new API and it's working. So let's run it. It's a bit frustrating to run a command which was running for quite a long time. I guess it can happen, but it is working and we can see that it does a pretty impressive job of just doing this migration in essentially one command, which is impressive. And let's get back to the presentation. Next is the complexity challenge. Large monolithic applications can have thousands of classes with interactive dependency graphs. Traditional refactoring approaches require manual analysis, service boundary identification and careful extraction planning. Processes are both time-incentive and error-prone.

Traditional refactoring approaches require manual analysis, service boundary identification, and careful extraction planning. Processes are both time-incentive and error-prone. Modern AI tools can analyze entire codebases simultaneously, identifying patterns that span multiple modules, understanding data flows across layers, and recognizing coupling patterns that indicate optimal service boundaries. Some enterprise success metrics are, for example, Pinterest that used AI to break a single Python codebase into 200 microservices, reducing deployment times by 70%. Shopify used AI-assisted extraction for their checkout service to reduce code complexity and improve performance. Netflix utilized automated boundary identification to decompose legacy systems with high accuracy.

Here's a practical example of how GPT-5 context can help handle API debugging scenarios. This code represents a typical enterprise controller with multiple dependent authentication, capture validation, database operation, and external services. It aims to refactor the component, replacing async await for a stack of callbacks. The demonstration focuses on starting small and resolving the callback issue within the component before expanding the refactoring across the codebase. The integration of AI tools into existing CI/CD pipelines enhances automatic quality gauges and enables continuous refactoring.

AI-powered static analyzers run on pre-commit hooks catching issues before reaching production. Tools like SORLD automatically fix code violations, while specialized linters understand domain-specific patterns and business rules. Continuous improvement through small refactoring changes proposed, validated, and deployed during regular development cycles is facilitated by AI. This incremental approach enables a shift from large-scale migration projects to ongoing enhancements, ensuring code quality and adherence to business rules.

You can see that our primary was passing the tests because it also has quite a comprehensive test battery covering all the edge cases, and what we did essentially is did a refactor and then it tried to go through a test battery but it found an issue. It is a perfect example of it trying to get through the issue by running tests through different scenarios. In that case, it was just a terminal issue, and after trying it for a while, it suggested that it fails with code 130 and can be tried manually, passing the test. By our standards, it is a perfect completion as we followed all the rules and got the expected result.

The most successful AI refactoring is not just standalone tools; they are integrated into existing CI/CD pipelines providing critical capabilities like automatic quality gauges. AI-powered static analyzers running on pre-commit hooks catch issues before production. Tools such as SORLD automatically fix code violations, and specialized linters understand domain-specific patterns and business rules, enabling continuous refactoring through small changes. AI also excels in testing and validation by generating comprehensive tests for refactored code, including edge cases that humans might miss. These tests validate the refactored code's behavior identically to the original, maintaining architectural requirements.

AI does not replace developers but amplifies their capabilities, requiring strong human oversight through mechanisms like human review, domain knowledge integration, and context about business rules, security requirements, and organizational constraints. Training AI on project-specific patterns and coding standards, risk management, staging environments for AI-generated changes, utilizing current deployments and automated fallback procedures, maintaining feature flags, and enabling quick disabling of problematic changes are essential for effective implementation.

Tools like for example SORLD automatically fix code violations, while specialized linters understand domain-specific patterns and business rules. They can also be used for continuous refactoring. AI enables continuous improvement through small changes proposed, validated, and deployed in regular development cycles, reducing risks and maintaining business continuity. AI is crucial for testing and validation, generating comprehensive tests for refactored code, including edge cases that humans might miss, validating behavior identically to the original code while maintaining architectural requirements.

AI does not replace developers but amplifies their capabilities, requiring strong human oversight through mechanisms like human review, domain knowledge integration, and context about business rules, security requirements, and organizational constraints. Effective implementation involves training AI on project-specific patterns, coding standards, and risk management. Staging environments for AI-generated changes, utilizing current deployments and automated fallback procedures, maintaining feature flags, and enabling quick disabling of problematic changes are essential practices.

AI refactoring presents challenges such as hallucinating imports or missing domain-specific constraints, potentially complicating complex business logic. Integration points between systems need careful human validation, and organizational challenges require addressing. Teams must be trained on AI-assisted development workflows, adapt code review processes to evaluate AI-generated changes, and overcome cultural resistance to automated transformations. Mitigation strategies include implementing comprehensive testing protocols, maintaining awareness of common AI mistakes, establishing clear governance over AI tools, investing in team training, and change management.

First of all is of course the review process. Every AI-generated change must go through human review, shifting the focus to validating business logic and architectural decisions. Domain knowledge integration is crucial, and AI tools need context about business rules, security requirements, and organizational constraints. The most effective approach involves training AI on project-specific patterns, coding standards, and risk management. Implement staging environments for AI-generated changes, utilize current deployments, automated fallback procedures, maintain feature flags, and allow quick disabling of problematic changes.

AI refactoring comes with its challenges. AI can hallucinate imports or miss domain-specific constraints, potentially complicating complex business logic. Integration points between systems require careful human validation, and there are organizational challenges to address. Teams need training on AI-assisted development workflows, adapting code review processes to evaluate AI-generated changes, and overcoming cultural resistance to automated transformations. Mitigation strategies include implementing comprehensive testing protocols, maintaining awareness of common AI mistakes, establishing clear governance over AI tools, investing in team training, and change management.

The trajectory toward increasingly sophisticated AI understanding of code context and business logic is evident. Moving towards AI agents handling multi-depositor transformations and architectural decisions with minimal human intervention is the goal. Immediate action items include starting small with low-risk refactoring tasks, investing in training, establishing governance, creating clear policies for AI tools usage, building infrastructure, implementing AI environments, and automated testing for AI changes. Tracking technical depth and productivity reduction is essential, as is experimenting with multiple AI tools, developing internal features, and prompt engineering and AI model configuration.