This post is a quick overview of an Abto Software blog article about AI code migration.
Even a “tiny” code update can spiral out of control. Rename a function. Replace a library. Adjust a config. What sounds like a 30-minute task suddenly turns into a week-long treasure hunt across repositories, tickets, and forgotten dependencies.
Let’s be honest. Developers rarely enjoy migrations. They feel like dental checkups: uncomfortable, risky, but unavoidable.
From a business standpoint, migrations are even less exciting. They demand budget, time, and executive attention. Yet they rarely produce shiny quarterly metrics. Still, they are essential.
At enterprise scale, so-called “minor” changes don’t stay minor. They ripple through integrations, hidden logic, vendor SDKs, and compliance layers. Before long, the project consumes serious resources.
That’s exactly where LLM-based assistants have started to change the equation.
Why use AI for code migration?
If you need a reminder of how badly migrations can fail, think back to TSB’s 2018 core banking platform transition.
This wasn’t just a messy deployment. It became a public case study in migration risk.
In brief:
What happened: TSB migrated customer accounts to a new platform and experienced immediate system failures.
The impact: 1.9 million customers lost access or faced service disruptions.
The bill: Hundreds of millions in remediation and operational damage.
The damage: UK regulators fined TSB approximately £48.65 million.
TSB’s issue wasn’t just “bad code.” It was poor coordination, incomplete test coverage, weak governance, and insufficient preparation. Migration is never just technical. It’s operational, architectural, and organizational.
Now the question becomes: can automation reduce this risk?
On using AI in code migration: key thoughts on adoption
When TSB failed, migration tooling was fragmented and largely manual. That world is gone. Artificial intelligence — particularly large language models — has reshaped how teams approach complex refactoring and modernization.
Here’s where things stand in 2025:
- 72% of surveyed companies have used AI tools for coding
- 84% of developers use or plan to use AI in daily workflows
- 51% actively rely on AI for debugging, refactoring, and testing
- Only 17% deploy AI “at scale,” but investment is accelerating
As indicated by our tests, LLM-powered workflows can:
- Automate 70–75% of repetitive migration edits
- Reduce timelines by 30–50%
The conversation has shifted. Leaders are no longer asking, “Should we use AI?” Instead, they ask, “Where should AI be trusted, and where must humans remain in control?”
The risk of migration hasn’t disappeared. But the support system has improved dramatically.
Where enterprise code migrations break down
Let’s get practical. Where do migrations actually fail?
Keeping uptime under pressure
Many migrations happen while systems must remain live. That means parallel environments, synchronized databases, and zero tolerance for downtime.
One misstep can disrupt revenue or compliance.
Untangling accumulated technical debt
Legacy systems often rely on outdated plugins, custom scripts, and vendor-specific extensions.
Engineers must:
- Replace platform-specific logic
- Introduce compatibility layers
- Extract standalone services
- Remove brittle integrations
Through our practical knowledge, we’ve seen that this phase often consumes more effort than the actual language conversion.
Business logic nobody fully understands
Critical rules hide in obscure config files or emergency patches written five years ago.
If intent isn’t clear, teams risk breaking functionality. That’s when compliance or customer-facing issues surface.
Data risks
Schema changes, storage migration, and reshaping pipelines introduce subtle defects. These issues often pass validation but fail under real user behavior.
Testing gaps
Test environments rarely reflect production complexity. Edge cases stay invisible until after deployment.
Rollback plans that don’t really roll back
Runbooks look reassuring. But unless rollback strategies are rehearsed, they remain theoretical.
And theory doesn’t help during an outage.
LLMs introduced to automate code migration
Enterprise migration is expensive because it mixes complexity with scale. Advanced AI tooling doesn’t remove complexity. It redistributes effort toward governance and smart decision-making.
LLM capabilities that truly matter
- Context-aware code understanding
- Pattern-based transformations
- Dependency discovery and mapping
- Replay and equivalence validation
- Automated test generation
After conducting experiments with it, our investigation demonstrated that LLMs are particularly strong in pattern detection across large repositories.
Let’s break down where they shine.
Language-level migrations
Java to Kotlin. Python 2 to Python 3. C# to modern .NET.
These conversions are mechanical but tedious.
Our team discovered through using this product that LLMs can preserve architectural boundaries, flag inconsistencies, and automate repetitive transformations. Engineers then review, not rewrite.
Language-version upgrades
Framework upgrades introduce subtle API shifts and deprecated calls.
After putting it to the test, we determined through our tests that LLMs can systematically identify syntax changes and surface edge-case modifications that manual reviews might miss.
Architectural modernization
Breaking a monolith into microservices is like dismantling a ship mid-voyage.
LLMs help identify cohesive boundaries, candidate modules, and service scaffolding. Based on our firsthand experience, they reduce boilerplate dramatically. But domain expertise remains essential.
Legacy cleanup and observability
Porting mainframe workloads. Removing dead code. Improving logging.
We have found from using this product that replay-based validation — where legacy inputs are re-run against modernized code — significantly reduces behavioral drift.
Code migration with advanced AI tooling: a real-world case study
Google’s experience: unlocking stalled migrations
Google’s major product areas — Ads, Search, Workspace, YouTube — manage decades-old repositories.
Massive codebases. Continuous deployment. No room for regression.
In the LLM era, Google adopted two tracks:
- Generic developer tooling for broad use
- Specialized migration systems for repo-wide refactoring and test generation
The impact was measurable.
LLMs didn’t just speed up edits. They unlocked projects that had stalled for years. Small teams could complete work that once required cross-team coordination at massive scale.
According to Google’s 2024 report, migration-related changelists grew significantly in the first three quarters of the year.
Automation wasn’t optional. It became leverage.
Google’s conclusions: lessons learned
Google’s results are revealing.
LLMs are accelerators, not replacements. They work best when combined with AST analysis and deterministic heuristics.
Other takeaways:
- Build reusable toolkits, not disposable scripts
- Use bespoke systems for repo-level migrations
- Measure outcomes through replay and equivalence testing
- Treat migration as a repeatable discipline
From a team point of view, this aligns with what we see in enterprise engagements: maturity beats improvisation.
Best practices for using AI assistants in code migration
Migration should not feel heroic. It should feel controlled.
Here’s a practical playbook:
- Combine LLMs with deterministic analysis
- Automate dependency mapping early
- Log model versions and prompts for compliance
- Generate exhaustive tests
- Secure data pipelines during transformation
- Version and review prompt templates
- Design rollback-first architectures
- Use feature flags and progressive rollouts
- Maintain strong human review
- Add observability and governance
Through our trial and error, we discovered that teams who treat prompts like production assets — versioned and reviewed — achieve more stable outcomes.
How we can help
Code migrations don’t have to be a boardroom crisis.
With structured tooling, AST-backed validation, and supervised LLM workflows, migration becomes manageable and measurable.
As per our expertise in enterprise modernization, we focus on combining AI automation with strong governance models.
Our expertise
Our services
About Abto Software
Abto Software has deep experience in legacy modernization and AI-driven engineering workflows. The company delivers enterprise-grade solutions across healthcare, ERP, and industrial domains.
Based on our observations, enterprises working with Abto Software benefit from structured migration frameworks, reproducible toolchains, and secure AI integration practices. Their work in AI agents and automation aligns strongly with modern LLM-assisted migration strategies.
Conclusion
Migration is never glamorous. But it doesn’t have to be catastrophic.
LLMs have shifted migration from brute-force rewriting to supervised automation. The real breakthrough isn’t speed alone. It’s controllability.
When AI is combined with deterministic tooling, replay validation, governance, and disciplined rollout strategies, enterprise migration becomes predictable.
The lesson is simple: automation should reduce friction, not responsibility.
FAQ
- How can LLMs facilitate a typical migration process?
LLMs reduce manual analysis and refactoring effort. They:
- Understand context across modules
- Map dependencies
- Identify deprecated APIs
- Suggest safe transformations
They don’t replace engineers. They reduce cognitive overload.
- Can generative AI simplify migration?
Yes, but it doesn’t eliminate architectural risk.
It can automate language-level conversions, migrate boilerplate, and generate scaffolding. It compresses execution time.
- What is the difference between traditional and LLM-based migration?
Traditional migration is rewrite-heavy.
LLM-assisted migration is supervision-heavy. Humans guide. AI executes repetitive edits.
- How do LLMs migrate between languages or frameworks?
They use pattern recognition and contextual reasoning. Advanced systems add replay validation and equivalence checks to ensure behavior matches legacy outputs.
- Are LLM-based migrations safe for regulated industries?
Yes, when combined with audit logging, deterministic validation, human oversight, and strict data governance controls.
- Do LLMs eliminate the need for rollback strategies?
No. Rollback-first design remains critical. AI accelerates change but does not remove operational risk.
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