How to Use LLM for Code Migration to Get Better Results

Quick Summary

Introduction

In today’s quick-moving digital scene, companies always look for ways to update their tech stack and make sure their systems stay strong, scalable, and safe. Code migration plays a key role in this, as it involves moving code from one platform or language to another. A client came to us at Bacancy Technology with a problem: they needed to migrate their big codebase in a way that would be quicker and more productive than old methods. Plus, they wanted the migration to be not just fast but also smooth and free of errors.

After talking about their needs and getting their pain points, we saw that using Large Language Models (LLMs) could help us get faster and more reliable results. LLMs can understand, create, and change code, which makes them the perfect fix. Here’s how we used LLMs to move the code and how it helped us work faster than old methods.

Common Challenges in Large-Scale Code Migration

Shifting a large and complex codebase is seldom a smooth experience. It usually has its own set of roadblocks that can slow down the process, add to the risk of bugs, and stretch resources out. Our client encountered a number of issues that are typical in many large-scale migrations:

Code Complexity:

The current codebase was massive and built over many years. It incorporated multiple programming languages, old frameworks, and home-built components. This combination made the transition extremely sensitive, as even minor changes to the code could break dependent modules. Refactoring or rewriting code portions had to be accomplished delicately for it to maintain functionality while being compatible in new environments.

Manual Errors:

Conventional migration methods are completely dependent on manual copying, rewriting, and translation of code by developers to work with the new stack. Working with thousands or even millions of lines of code, a smaller human mistake can later lead to major issues. It is difficult to detect these in real-time and can delay progress or introduce bugs that are difficult to debug at deployment time.

Time Constraints:

The migration was not a technical project alone, and it had immediate business consequences. The client did not have much time to migrate their system to a new architecture without affecting day-to-day business. Any delays would have resulted in downtime, lost business, or even customer dissatisfaction. Time was of the essence, but an unnecessary rush without appropriate tools could’ve turned things around for the worse.

Lack of Expertise:

One of the technologies in the migration was not well known to the client’s internal developers. Though they knew the old system, they did not have profound insight into the newer stack that the company was migrating to. This created a skill gap that could have resulted in more extended development time, higher reliance on external assistance, and potential architecture decision mismatches.

The objective before us was obvious: complete the migration quicker, minimize manual mistakes, and have the code functioning well in its new environment. These types of challenges appear frequently in large-scale projects, but they don’t need to hold things back. Hire LLM engineers from Bacancy to complete code migration smoothly and without additional hassle.

How We Used LLM to Help Move Code

As we were well aware of the hurdles ahead of us, we created a process on top of LLMs to facilitate smoother code migration, reduce the time taken, and cut down on errors. Instead of taking over from developers, LLMs worked alongside developers, accelerating repetitive sections and enabling us to remain consistent on a huge amount of codebase. Below is the step-by-step process followed by us:

Code Analysis and Preprocessing

Our first step was to break down the current codebase to learn its complete architecture. We employed LLMs to search through thousands of lines of code, recognize frameworks utilized, detect deprecated functions, and locate patterns across modules. This process provided us with a roadmap, what could be reused, what required rewriting, and what dependencies needed to be dealt with delicately.

Automated Code Translation

After we had a good understanding, we proceeded to the actual code translation. The LLM assisted in translating legacy code into modern frameworks or a new language, based on the client’s target architecture. Due to its context-aware nature, the model was not only translating syntax but also maintaining logic. It prevented us from the usual pitfalls of manual conversion such as broken logic paths or missed files.

Code Refactoring Support

Apart from translation, we employed LLMs to support intelligent refactoring, proposing neater structures, detecting code odors, and indicating duplicate blocks. The step was not merely about rewriting code but enhancing its quality overall at the time of migration.

Testing and Validation

After migration, we used the LLM to generate unit tests, validate code integrity, and even help debug where needed. This helped us catch potential breakpoints early and saved valuable time in post-migration QA. In some cases, LLMs helped regenerate test cases that had been lost or outdated in the legacy system.

Documentation Support

Legacy systems usually lack proper documentation. LLMs helped us automatically generate documentation for the newly migrated code, making it easier for the client’s team to maintain and understand the code in the future. This step also helped with smoother handoffs and reduced dependency on any specific team members.

Performance Optimization Suggestions

Once the code was up and running, LLMs also participated in performance testing. They suggested possible optimizations, such as minimizing repetitive queries, rewriting inefficient loops, and enhancing modularity, which made the code lighter and ready for scaling.

Factors That Sped Up Code Migration

A few key things helped us migrate code faster with LLMs:

Automation: LLMs helped us automate big chunks of the code migration process. Jobs that took days or weeks finished much faster.

Context-Aware Code Generation: LLMs grasped the code’s context making sure the new code was not just correct in syntax but also made sense in context. This cut down on mistakes and do-overs.

Real-Time Feedback: Thanks to LLMs, we could spot problems and fix them right away instead of waiting for long test runs. This boosted both speed and accuracy.

Parallel Processing: LLMs let us split migration tasks into smaller parallel chunks, which sped up the whole process. This helped in big migrations.

Continuous Learning: As LLMs saw more data and code during migration, they kept getting better. They adapted to the client’s specific coding styles and needs.

These elements worked together to cut down migration time and reduce mistakes, leading to quicker and more productive code migration.

How LLMs Helped Us Give Our Clients Better Code Migration Outcomes

At Bacancy Technology, we’re proud to deliver results that go beyond what our clients expect, and this project was no exception. By bringing LLMs into the migration process, we were able to:

• Complete the Migration Faster: The client worried about a tight deadline, but LLMs did most of the hard work. This sped up the migration, and we finished much quicker than usual methods.
• Ensure Code Accuracy and Quality: LLMs translated and tested the code. This made sure the new code worked well and ran fast giving the client what they needed.
• Minimize Downtime: The quicker migration meant less disruption to the business. We got the client’s systems back up and running with very little downtime.
• Offer Immediate Help and Input: As we moved the code, LLMs helped us keep improving and fine-tuning it. This made sure we always stayed on track with what the client wanted and needed.

In the end, our use of LLMs helped us complete a successful code migration that met the client’s needs faster than we first thought.

Things to Remember During Code Migration with LLM

While LLMs assisted in making the migration process quicker and more efficient, there are still significant things to remember to make sure everything goes well and does not fall into common traps:

Too Much Trust in Automation

LLMs are strong and can perform various tasks but require human guidance. Avoid completely relying on automation, particularly for architectural choices, business logic, or instances that need critical thinking. Developers ought to always double-check the outputs and ensure the system fits into the larger objectives of the project.

Not Enough Testing

Automated testing is great, and LLMs can help generate and run many tests quickly. However, they might miss corner scenarios or edge cases that aren’t well represented in the training data. Manual QA and reviews are necessary to ensure the final code performs well in real-world environments.

Bad Data Preparation

The output quality of an LLM is only as good as the input it receives. If the original codebase is messy, unorganized, or poorly documented, LLMs may not understand it well or might make incorrect assumptions. Cleaning up the code, removing redundancies, and ensuring clear documentation beforehand can significantly improve migration accuracy.

Avoiding Legacy Dependencies

Several of the older systems rely on outdated libraries, APIs, or hardware-specific implementations. LLMs can struggle to deal with or replace such dependencies automatically. Finding and resolving such dependencies by hand or through expert assistance is critical to successful migration.

Through this awareness, we maintained our LLM-powered migration on track and sidestepped the common pitfalls that can slow down or derail it. It’s all about the fusion of AI might with astute planning and experienced developers.

Final Thoughts