Maintaining a legacy web codebase has traditionally been one of the most daunting responsibilities in software engineering. Many enterprise systems run on code that was written a decade or more ago, sometimes by developers who have long since left the organization. Legacy code often becomes a hidden obstacle to growth and innovation.
These codebases often use outdated frameworks, inconsistent naming conventions, and brittle architectures that make even the smallest changes risky. For years, organizations relied on manual refactoring, patchwork documentation, and slow migration cycles.
But with the arrival of large language models (LLMs), the way we approach legacy web refactoring and maintenance is undergoing a radical transformation.
In this article, we’ll explore how LLMs can help in the maintenance and refactoring of legacy web systems in 2025.
The challenge of legacy web systems
Most legacy web applications weren’t designed with modern best practices in mind. Developers commonly encounter:
- Monolithic architectures where tightly coupled modules make feature upgrades dangerous.
- Obsolete frameworks and libraries like jQuery spaghetti code, or deprecated PHP versions.
- Inconsistent code quality due to years of patching, urgent hotfixes, and lack of unified style guides.
- Missing documentation, making it nearly impossible to understand why certain design decisions were made.
- Integration fragility, where one change in a template or database query can break half the system.
Traditionally, addressing these issues required teams of senior engineers spending months analyzing the code, writing migration plans, and executing incremental rewrites. It was error-prone, expensive, and exhausting.
Reasons why refactoring legacy code is difficult
Refactoring legacy code is a lengthy, expensive, and resource-intensive task. It is also full of risks because developers have to manually rewrite everything from logic to UI components. As a result, there are often delays and errors during the code modernization process.
Here are some of the main reasons why refactoring legacy code is quite challenging:
1. Tangled code
Over years of fixes and patches, unrelated parts of the system get tied together. A small change in one place can ripple through the whole system, causing unexpected breakages. Therefore, developers usually don’t touch working code, which hinders modernization.
2. No safety nets
Many older codebases were built before quality assurance and testing were standard practices. Without automated unit or integration tests, any refactor becomes risky. To make things worse, old version control histories may be incomplete, so developers can’t even trace why a piece of code exists.
3. Outdated technology
Some web systems still run on very old frameworks or languages that are either obsolete or don’t work well with modern tools. They may require outdated compilers that modern machines no longer support.
Just compiling the code can be a nightmare, let alone updating it to use modern practices.
4. Abstruse logic
Important business logic can be buried thousands of lines of code. They can be written by people who have left the company long ago. If there is little or no documentation, the development team can spend weeks deciphering the intent before making safe changes.
5. Unpredictable behavior
Legacy code often relies on background jobs, which means a function that looks harmless may actually be changing hidden data. Therefore, a small refactor can unintentionally change the web system’s whole behavior.
How AI is modernizing legacy tech
Until recently, the only way to truly understand a legacy codebase was to read it, trace its execution, and manually write notes. The rise of LLMs changes this dynamic. These models can read code, explain it in natural language, and even propose safe improvements.
Here’s what’s now possible:
1. Automated documentation generation
LLMs can scan a function or class and explain in plain English what it does. They can generate README files, code comments, and architectural overviews without weeks of manual effort.
For example, you can paste an obscure PHP function into an AI tool and ask:
“Explain what this function does, including its parameters, return values, and side effects. Suggest clearer variable names.”
Within seconds, you have a human-readable explanation and naming recommendations. For teams inheriting code from contractors or past employees, this drastically reduces ramp-up time.
2. Navigating and understanding a codebase
One of the most time-consuming aspects of working with a legacy system is navigation. Developers are often forced to traverse hundreds of files, trace interdependent modules, and manually build a mental map of the system.
Traditional tools like grep or IDE-based search functions can locate keywords, but they provide no semantic understanding of why a function is called or how components are linked.
With the help of LLMs, instead of sifting through results manually, an engineer can now ask targeted questions such as:
- “Which files call the legacyAuth() function, and under what conditions?”
- “List the modules that depend on the OrderManager class and summarize their responsibilities.”
- “Explain the full sequence of operations in the payment processing flow, including database interactions and error handling.”
The model not only retrieves locations but also explains the relationships between them. For example, rather than just listing all invocations of legacyAuth(), the model might highlight that it is called primarily in checkout.js and userSession.php, describe how authentication tokens are passed downstream, and identify potential inconsistencies between modules.
In practical terms, this transforms hours of manual exploration into a few structured prompts and responses, giving developers a holistic view of the system far more quickly.
3. Finding and explaining bugs
Traditional static analysis tools such as ESLint for JavaScript are proficient at identifying syntax errors, unused variables, or known anti-patterns. However, they generally output terse warnings, leaving engineers to interpret the significance and decide on corrective action. This is where LLMs add value: they provide explanatory context and proposed resolutions in plain language.
LLMs can also flag higher-order issues that go beyond syntax, such as circular dependencies, improper error handling, or functions that silently catch exceptions without logging. Unlike static tools, which typically output isolated warnings, a model can trace the impact of these bugs across the codebase, showing how a silent exception in one module might cascade into a payment system failure downstream.
This layered explanation allows engineers not only to fix defects but also to understand their systemic impact, making long-term remediation strategies more effective.
4. Migration support
One of the most expensive aspects of legacy maintenance is migration. Moving from AngularJS to Angular, replacing raw SQL queries with an ORM, or updating APIs across a repository all demand repetitive, error-prone edits.
Google’s case study illustrates this point clearly. Faced with migrating identifiers from 32-bit to 64-bit integers across its massive codebase, Google built a workflow that combined automated search with LLM-powered transformation. References were identified using their Kythe indexing system, categorized by confidence, and then processed by a fine-tuned Gemini model.
Candidate changes were automatically validated through syntax checks, AST parsing, and full test suites before reaching human reviewers. Developers only intervened in ambiguous cases. What previously required years of manual labor was reduced to a fraction of the time, without loss of quality.
Results from one year of production use:
- 39 migrations completed.
- 595 code changes and 93,574 edits across multiple projects.
- LLMs successfully produced 74% of changes and 69% of edits.
- Developer-reported time savings of approximately 50% compared to manual migration.
The Future: AI as a co-maintainer
Looking ahead, AI won’t just be a one-off assistant. Organizations are embedding LLMs as continuous co-maintainers, running in CI/CD pipelines. These AI models constantly monitor for technical debt, suggest dependency updates, enforce style consistency, and pre-generate migration patches before developers even request them.
As models improve, we can expect AI to take on more autonomous responsibilities, such as automatically patching vulnerabilities and proposing architecture upgrades in real time.
Conclusion
Legacy web maintenance has always been viewed as a tedious, high-risk endeavor. But with the rise of AI-powered maintenance, particularly through LLMs, organizations can now treat it as an opportunity for continuous modernization rather than a painful necessity.
LLM-powered code refactoring works on a simple principle: preserve what works and optimize what doesn’t. LLMs are able to do that by automating modernization, assisting in bug detection, and reconstructing lost documentation. They are thus transforming the economics and efficiency of maintaining old systems.
If you’re struggling with refactoring your legacy code, Xavor can help you expedite the process with its AI services. Our AI engineers and programmers use the latest LLMs to modernize your legacy applications quickly and efficiently.
To connect with our AI experts, contact us at [email protected]