Top 13 AI Tools to Master for Developers Refactoring a Spaghetti Code Monolith
We've all been there. Staring into the abyss of a legacy monolith, a tangled web of dependencies so complex it makes a bowl of spaghetti look like a grid system. The code has no tests, the original developers are long gone, and every small change feels like a high-stakes game of Jenga. This is the dreaded spaghetti code monolith, the final boss of technical debt.
For years, refactoring such a beast was a Herculean task, requiring months of careful archaeology, painstaking manual rewrites, and a healthy dose of courage. But the landscape is changing. The rise of powerful, developer-focused AI tools is transforming this daunting challenge into a manageable, even exciting, process. These AI assistants act as your co-pilot, helping you understand, untangle, and modernize code at a speed we could only dream of a few years ago.
This isn't about letting AI write your code for you. It's about augmenting your own expertise. It's about having a tireless partner that can map dependencies, suggest improvements, generate boilerplate tests, and explain arcane logic in plain English. In this guide, we’ll explore the top 13 AI tools that will become your secret weapons in the war against spaghetti code. Let's dive in.
1. GitHub Copilot
GitHub Copilot is more than just an autocomplete tool; it's an AI pair programmer living inside your IDE. While famous for generating new code, its true power in a refactoring context lies in its ability to understand existing code and provide intelligent suggestions. It’s the first tool most developers reach for, and for good reason.
When you're faced with a 500-line function that does ten different things, Copilot can be your brainstorming partner. You can highlight the entire block of code and ask it, "Refactor this to follow the Single Responsibility Principle." It won't always give you a perfect, production-ready answer, but it provides a fantastic starting point, often identifying logical chunks that can be extracted into separate, more manageable functions. It also excels at generating boilerplate, like converting a messy block of code into a well-structured class.
- Pro Tip: Use the integrated Copilot Chat. Instead of just accepting suggestions, you can have a conversation about the code. Ask it questions like,
/explain what this legacy function does,/tests generate unit tests for this selected code, or/fix improve the readability of this code. This conversational approach makes refactoring a much more interactive and less isolating process.
2. Sourcegraph Cody
If GitHub Copilot is your pair programmer, Sourcegraph Cody is your team's lead architect with a photographic memory of the entire codebase. Its superpower is its deep understanding of your entire repository, not just the file you have open. This is absolutely critical when refactoring a monolith where a change in one file can have catastrophic, unforeseen consequences ten modules away.
Cody builds a comprehensive graph of your codebase, allowing you to ask complex questions about dependencies and logic flows. For a spaghetti monolith, this is a lifesaver. You can ask, "Where is this deprecated processPayment() function being called?" or "What is the call stack for this API endpoint?" Cody will give you precise answers with links directly to the relevant code, saving you hours of manual grep-ing and code spelunking.
- Example: Imagine you need to refactor a core
Usermodel. Before you touch a single line, you can ask Cody, "Show me all the services that directly access theUser.profile.emailproperty." This allows you to understand the blast radius of your change and plan your refactoring strategy with confidence.
3. MutableAI
MutableAI is a tool specifically engineered for the pains of modernizing and refactoring legacy codebases. It moves beyond simple suggestions and offers a more production-oriented workflow, focusing on creating pull requests with AI-suggested refactors, along with the corresponding tests to validate them. This focus on testing is what makes it a standout for monolith refactoring.
Its "AI-assisted refactoring" feature can analyze a section of code and propose significant improvements, such as migrating a large function to a new, cleaner architecture or converting an old script to use modern language features. Crucially, it doesn't just change the code; it also attempts to generate or update unit tests to ensure the behavior remains consistent. This helps build the test coverage that is so often missing in legacy systems.
- Pro Tip: Use MutableAI to build your safety net. Before you start a major refactoring effort, point it at key business logic modules and use its test generation capabilities. Even if the generated tests aren't perfect, they provide a baseline you can refine, ensuring you don't break critical functionality as you untangle the spaghetti.
4. Amazon CodeWhisperer
As a direct competitor to GitHub Copilot, Amazon CodeWhisperer offers a robust set of features for code completion, explanation, and security scanning. Its key differentiator is its deep integration with the AWS ecosystem and its focus on security. When refactoring a monolith, you're not just dealing with bad structure; you're often dealing with outdated security practices.
CodeWhisperer can scan your code for potential security vulnerabilities, such as hardcoded secrets, injection flaws, or improper use of cryptographic libraries. As you rewrite and refactor old code, it acts as a security guard, flagging potential issues in real-time. This is invaluable, as it prevents you from carrying old security debt into your new, cleaner architecture.
- Example: While refactoring an old database connection script, CodeWhisperer might flag that you're concatenating strings to build a SQL query (a classic SQL injection vulnerability). It would then suggest using a parameterized query instead, providing the corrected code snippet on the spot.
5. Tabnine
Tabnine was one of the early pioneers in AI code completion, and it remains a powerful and highly customizable option. Its strength lies in its ability to learn from your specific codebase and coding patterns. You can train Tabnine on your own repositories, allowing it to provide suggestions that are highly tailored to your project's unique conventions, variable names, and architectural style.
In a large monolith, consistency is often a huge problem. Different parts of the codebase, written by different developers over a decade, may follow completely different styles. As you refactor, Tabnine can help enforce a new, consistent style. By learning from the clean code you're writing, it will start suggesting code that follows the new patterns, helping to propagate good practices throughout the refactoring process.
- Pro Tip: Configure Tabnine to connect to your organization's entire codebase. This gives it the context it needs to provide truly relevant suggestions, making it feel less like a generic tool and more like a seasoned team member who already knows your project inside and out.
6. Stepsize AI
Refactoring isn't just about changing code; it's about managing technical debt. Stepsize AI is a tool built specifically for this purpose. It integrates with your IDE and issue tracker (like Jira) to help you identify, prioritize, and fix technical debt, including the kind found in spaghetti code.
Stepsize allows you to create "issues" directly from your code. See a messy function that needs refactoring? Highlight it, and Stepsize lets you create a trackable issue linked directly to that code block. Its AI capabilities come in when analyzing the codebase to identify hotspots—areas with high churn, complexity, and potential bugs—which are prime candidates for refactoring. It helps you answer the crucial question: "Where should we start?"
- Example: Stepsize's dashboard might highlight a file named
utils.jsas a major source of tech debt because it's been modified in hundreds of pull requests and is linked to numerous bug reports. This data-driven insight tells you that refactoring this "god file" will likely have the biggest positive impact on your team's productivity and system stability.
7. CodeRabbit
Code reviews are a critical part of a successful refactoring process. They ensure the new code is actually better than the old code. CodeRabbit is an AI-powered code reviewer that integrates with GitHub and GitLab to provide line-by-line feedback on pull requests. It can catch issues that human reviewers might miss, especially when reviewing large refactoring PRs.
CodeRabbit provides contextual summaries of changes, which is incredibly helpful for reviewers trying to understand the scope of a complex refactor. It can spot potential bugs, suggest performance improvements, and ensure the code adheres to best practices. By automating the first pass of a code review, it frees up senior developers to focus on higher-level architectural feedback rather than nitpicking syntax.
- Pro Tip: Configure CodeRabbit with your team's specific coding guidelines. This ensures that as you refactor the monolith, the new code being introduced is consistently high-quality and adheres to the standards you're trying to establish.
8. Snyk Code
Security is paramount when modernizing a legacy application. Snyk Code is a developer-first static application security testing (SAST) tool that uses AI to find and fix security vulnerabilities in your code. It provides real-time feedback in your IDE, making it easy to address security issues as you encounter them during refactoring.
Monoliths are often littered with outdated dependencies and vulnerable coding patterns. Snyk's AI engine is trained on a massive database of vulnerabilities and can identify complex security flaws that other tools might miss. It not only tells you what is wrong but also provides actionable advice and code examples on how to fix it.
- Example: While refactoring a file upload module, Snyk might detect that you are not properly sanitizing file names, creating a potential path traversal vulnerability. It will flag the exact line of code, explain the risk, and suggest a code snippet using a library that properly handles sanitization.
9. Mintlify Writer
One of the biggest problems with spaghetti code is the complete lack of documentation. What does this function do? Why was it written this way? What are its side effects? Mintlify Writer is an AI tool that helps solve this problem by automatically generating documentation for your code.
You can highlight a function, a class, or an entire file, and Mintlify will generate clear, concise docstrings and comments explaining what the code does. While it can't know the original business intent, it does an excellent job of describing the code's function. This is a huge time-saver and a critical first step in making a complex codebase understandable to the rest of the team. As I've often seen in projects I've advised on with Goh Ling Yong's consulting clients, clear documentation is the first step to successful refactoring.
- Pro Tip: Use Mintlify in two phases. First, run it on the old, undocumented code to get a baseline understanding. Then, as you refactor the code to be clearer and more modular, use Mintlify again to generate fresh, accurate documentation for the new, improved code.
10. Bito AI
Bito AI positions itself as a "Swiss Army Knife" for developers, aiming to be the "ChatGPT for developers" that lives inside your IDE. It's built on top of large language models but is specifically fine-tuned for a wide range of developer tasks, making it a powerful general-purpose tool for refactoring.
Bito can do everything from explaining code and generating comments to creating unit tests and even writing release notes. One of its most useful features for refactoring is its ability to "diff" and explain changes. You can feed it a before-and-after code block and ask it to explain the refactoring that was done. This is great for writing descriptive pull request summaries or for getting up to speed on refactoring work done by other team members.
- Example: You just spent two hours refactoring a complex algorithm. Instead of manually writing a detailed explanation, you can ask Bito: "Explain the performance improvements in this refactored code compared to the original." It will analyze the changes and provide a clear, technical explanation you can use in your documentation or PR.
11. Diffblue Cover
For teams working with Java, Diffblue Cover is a game-changer. Refactoring without a solid test suite is like flying blind. Diffblue uses AI to autonomously write unit tests for existing Java code. It analyzes your code, understands the behavior, and then generates a full suite of JUnit tests that reflect that current behavior.
This process, known as characterization testing, is fundamental to safe refactoring. You run Diffblue on a module before you refactor it to create a baseline of its behavior. Then, after you refactor the code, you run the same tests again. If they still pass, you have a high degree of confidence that you haven't introduced any regressions. This automates the most tedious part of preparing a legacy system for modernization.
- Pro Tip: Don't treat Diffblue's output as the "perfect" test suite. Treat it as a massive head start. Review the generated tests to ensure they cover the most critical business logic, and add your own specific edge-case tests where needed.
12. AskCodi
AskCodi is another versatile AI assistant that provides a suite of tools useful for understanding and improving legacy code. It has dedicated features for code explanation, test generation, and documentation, but its "Code Extender" feature is particularly interesting for refactoring.
The Code Extender allows you to start with a piece of code and ask the AI to build upon it. For example, you might have a simple, messy function and ask AskCodi to "Extend this function to include error handling and logging." This helps you methodically add the robustness and production-readiness that is often missing from old, fragile code without having to write all the boilerplate from scratch.
- Example: You find a function that makes a network request but has no
try-catchblock. Instead of writing it yourself, you can use AskCodi to wrap the existing logic in a robust error-handling block, complete with logging for different failure scenarios (e.g., network timeout, 404 error, 500 error).
13. ChatGPT / Claude (The AI Consultants)
Finally, we can't ignore the general-purpose titans: ChatGPT (with GPT-4) and Anthropic's Claude. While not IDE-integrated tools in the same way as the others, they are incredibly powerful "AI consultants" you can use for high-level strategic planning. They are invaluable for brainstorming architectural changes.
You can describe a problematic module in your monolith in plain English, paste in sanitized code snippets, and ask for advice. "I have this module that handles user authentication, payment processing, and profile updates. This feels wrong. What design patterns, like the Strategy or Facade pattern, could I use to break this up into smaller, more maintainable services?" These models can provide detailed architectural suggestions, pseudo-code, and explanations of trade-offs.
- Pro Tip: Use these models for "rubber duck debugging" on a massive scale. When you're stuck on a complex refactoring problem, spend 15 minutes writing out a detailed prompt explaining the situation, the constraints, and what you've tried so far. The act of writing the prompt itself often clarifies your thinking, and the AI's response can provide the breakthrough idea you were missing.
Conclusion: Your AI-Augmented Refactoring Journey
Untangling a spaghetti code monolith will never be "easy," but it no longer has to be a soul-crushing, career-defining ordeal. The AI tools on this list act as powerful force multipliers, automating the tedious, accelerating the complex, and illuminating the obscure. They handle the grunt work of generating tests, writing documentation, and spotting boilerplate errors, freeing you up to focus on what truly matters: a clean, robust, and scalable architecture.
The key is to think of these tools not as replacements for your skills, but as enhancements to them. You are still the architect, the strategist, and the final decision-maker. This new generation of AI simply gives you a much, much better toolkit to work with. Start by picking one or two tools from this list that address your biggest pain points, integrate them into your workflow, and watch your refactoring efforts go from a slow, painful crawl to a confident, accelerated sprint.
What are your go-to AI tools for tackling legacy code? Did I miss any of your favorites? Share your thoughts and experiences in the comments below—I'd love to hear from you!
About the Author
Goh Ling Yong is a content creator and digital strategist sharing insights across various topics. Connect and follow for more content:
Stay updated with the latest posts and insights by following on your favorite platform!