Top 14 'Code-Archaeology' AI Tools to explore for Developers Modernizing Legacy Systems in 2025 - Goh Ling Yong
Ever felt like an archaeologist, but instead of digging for ancient artifacts, you're excavating layers of decades-old code? You blow the digital dust off a function, and the last commit was by a developer who retired before you even learned to code. There are no maps, no documentation, and the original architects are long gone. This is the world of legacy system modernization, a field many of us affectionately call 'code-archaeology'.
For years, this process has been a slow, painstaking, and often risky endeavor. Deciphering tangled business logic, untangling dependencies, and refactoring brittle code required immense manual effort and a healthy dose of courage. But the landscape is dramatically shifting. The rise of sophisticated AI is providing us with a new toolkit—a set of ground-penetrating radar, digital carbon-dating, and intelligent chisels—to make this process faster, safer, and more insightful.
Here on the Goh Ling Yong blog, we're diving deep into the tools that are transforming this critical discipline. As we look ahead to 2025, these AI-powered assistants are no longer just novelties; they are becoming indispensable partners for any developer tasked with breathing new life into a legacy system. Here are the top 14 'code-archaeology' AI tools you should have on your radar.
1. GitHub Copilot Enterprise
GitHub Copilot Enterprise has evolved far beyond its origins as a simple code completion tool. It now leverages the entire context of your organization's private repositories, making it a formidable ally when navigating a sprawling, unfamiliar legacy codebase. It's like having a senior developer who has already memorized every line of code in the company.
For the code-archaeologist, this is game-changing. Instead of spending days tracing a function call through a dozen files, you can simply ask Copilot, "@workspace How is the calculate_interest function used in the billing module?" It can analyze internal libraries, understand proprietary frameworks, and generate summaries of complex code blocks in plain English. This ability to query your entire codebase with natural language turns daunting exploration into a guided conversation.
Pro Tip: Use Copilot Enterprise's documentation generation feature on a particularly obscure, uncommented module. Select the code and ask it to "generate docstrings and a high-level summary." This creates a foundational document you can refine, saving you hours of manual reverse-engineering.
2. Sourcegraph Cody
If GitHub Copilot is your partner, Sourcegraph Cody is your library and search engine on steroids. Sourcegraph has always been the gold standard for code search across massive monorepos, and Cody injects a powerful AI layer on top of that. It excels at understanding the "how" and "why" behind the code.
Cody's real strength in a legacy context is its ability to understand code health and identify areas ripe for refactoring. You can ask it to "find all usages of this deprecated API and suggest a modern replacement," and it will not only find them but also help you draft the refactored code. It's an expert at finding code smells, identifying anti-patterns, and explaining the purpose of code that was written before you were born.
Example: You've been tasked with removing an old, insecure logging library. Use Cody to find every single instance of it across multiple repositories. Then, ask it, "What is the standard logging practice in this codebase?" Cody will analyze existing patterns and help you implement the new library consistently.
3. Amazon CodeWhisperer
While similar to Copilot, Amazon CodeWhisperer distinguishes itself with its deep integration into the AWS ecosystem. This is a crucial advantage for modernization projects that involve migrating a legacy application from on-premise servers to the cloud. It's trained on a massive corpus of code, including Amazon's own codebases and open-source projects.
CodeWhisperer is your expert guide for rewriting old code to use modern, cloud-native services. For instance, if you're replacing an old file I/O system, it can suggest the correct way to implement it using Amazon S3 SDKs, complete with proper authentication and error handling. It also includes a reference tracker that can flag code suggestions that resemble open-source training data, helping you maintain code integrity and avoid licensing issues.
Tip: When refactoring a legacy Java application for AWS, use CodeWhisperer's "security scan" feature. It will analyze your code to find vulnerabilities like hardcoded credentials or inefficient API calls before you deploy them to the cloud, saving you from a world of security headaches.
4. vFunction
vFunction is a specialized AI platform designed for one of the most challenging parts of modernization: breaking down a monolithic application into microservices. It performs a deep, dynamic analysis of a running legacy application (often in Java or .NET) to identify logical domains and service boundaries automatically.
This tool acts as a CT scanner for your monolith. It observes the application at runtime, mapping out dependencies, class interactions, and data flows. The AI then presents you with a visual architecture of your system and recommends potential microservices. It quantifies the "exclusivity" of classes to a specific service, helping you make data-driven decisions about where to draw the lines. This dramatically de-risks the strangler fig pattern approach to modernization.
How to Use It: Run the vFunction agent on your legacy application in a staging environment. Let it collect data for a few days to capture various use cases. The AI-driven analysis will then give you a report suggesting, for example, that all the classes related to "payment processing" are highly intertwined and would make an excellent first microservice to extract.
5. IBM watsonx Code Assistant for Z
For the true archaeologists dealing with the mainframes of yesteryear, IBM watsonx Code Assistant for Z is an absolute marvel. Its primary mission is one of the most classic and feared modernization tasks: translating legacy COBOL code into modern, high-quality Java. This is a highly specialized skill set, and this tool automates a massive portion of it.
The AI doesn't just do a blind, line-by-line translation. It's trained to understand the business logic locked within the COBOL programs. It can identify and refactor COBOL paragraphs and sections into logical Java methods, preserving the original intent while creating a maintainable, object-oriented structure. For organizations with millions of lines of COBOL, this tool can cut modernization timelines from years to months.
Example: You have a massive COBOL program that handles complex financial calculations. Instead of manually deciphering the COMPUTE statements and PERFORM loops, you feed it to watsonx. It analyzes the code, understands the business logic, and generates a set of Java classes and services that are optimized for performance and ready to be integrated into a modern cloud environment.
6. Mutable.ai
Mutable.ai is an AI-powered tool laser-focused on improving code quality and accelerating development through intelligent refactoring. It connects directly to your codebase and acts as an automated junior developer, creating pull requests for you to review and merge.
In a legacy project, Mutable.ai can be set up to systematically chip away at technical debt. It can add types to a legacy JavaScript/TypeScript codebase, migrate an entire codebase to a new testing framework (e.g., from Jest to Vitest), or refactor large functions into smaller, more manageable ones. It learns from your existing code style to ensure its suggestions are consistent and idiomatic.
Tip: Point Mutable.ai at a legacy Python 2 codebase. Use its "modernization" recipes to automatically generate pull requests that convert the code to Python 3, updating syntax, libraries, and common patterns. This automates a tedious and error-prone process.
7. CodeSee
While many tools help you read code, CodeSee helps you see it. It's an AI-powered visualization tool that automatically generates interactive diagrams of your codebase's architecture, dependencies, and data flow. For the code-archaeologist, this is like unrolling the original blueprints of a building you're trying to renovate.
When you're first approaching a massive, undocumented legacy system, running CodeSee can give you that crucial high-level overview. You can visually identify hotspots of complexity, trace user journeys through the code, and understand the impact of a potential change before you write a single line. It's especially useful for onboarding new developers to the project, drastically reducing their ramp-up time.
How to Use It: Before starting a major refactoring, use CodeSee's "Codebase Maps" to visualize the module you're about to change. See all its incoming and outgoing dependencies. This visual map helps you identify potential side effects and ensures you don't break something in a seemingly unrelated part of the application.
8. Snyk Code
Modernization isn't just about updating frameworks and languages; it's about making the system secure for the modern world. Snyk Code is a static application security testing (SAST) tool that uses AI to find security vulnerabilities with incredible accuracy and speed.
Legacy code is often a minefield of outdated dependencies with known vulnerabilities and insecure coding practices from a bygone era (hello, SQL injection!). Snyk's AI engine is trained on a massive dataset of security issues and can provide context-rich explanations and actionable fixes. It understands the flow of data through your application, allowing it to spot complex vulnerabilities that other tools might miss.
Archaeology Tip: Run a Snyk Code scan on your entire legacy repository as your very first step. This will generate a "treasure map" of the most critical dangers lurking in the code. Prioritize fixing these high-severity security issues as part of your modernization roadmap.
9. AskCodi
Documentation is often the first casualty in software development, and in legacy systems, it's usually non-existent. AskCodi is an AI assistant that excels at understanding and explaining code, generating documentation, and creating test cases. It's the universal translator for your archaeological dig.
Imagine you find a complex SQL query embedded in a string within a 20-year-old Perl script. You can paste that query into AskCodi and ask it to "explain this SQL query in plain English." It can also generate unit tests for a function that has zero test coverage, giving you a safety net before you attempt to refactor it.
Example: You have a C++ function full of bit-shifting and pointer arithmetic. Use AskCodi's "Explain Code" feature to get a step-by-step breakdown of what the function is actually trying to accomplish. This is infinitely faster than manually stepping through with a debugger.
10. Tabnine
Tabnine has been a player in the AI code completion space for a while, but its strength for legacy modernization lies in its privacy and personalization features. Many legacy systems contain highly sensitive business logic or data, and enterprises are hesitant to send that code to a cloud-based AI.
Tabnine offers on-premise or VPC deployment options, allowing you to train its AI model securely on your own private codebase. This means it learns the specific patterns, naming conventions, and proprietary APIs of your legacy system without the code ever leaving your control. It becomes a true expert in your archaic code, providing highly relevant suggestions that generic models can't match.
Tip: Once you've deployed Tabnine and trained it on your legacy monorepo, pay attention to its "full function" completion suggestions. It will often generate entire boilerplate functions based on your internal conventions, speeding up the process of adding new, consistent code to an old system.
11. Diffblue Cover
One of the biggest fears in modernizing legacy code is making a change that has unintended consequences. The best way to mitigate this is with a comprehensive suite of unit tests, but legacy systems rarely have them. Diffblue Cover is an AI-powered tool that automatically writes Java unit tests for you.
Diffblue analyzes your existing Java code, understands the different paths and branches, and generates complete, ready-to-run JUnit tests that reflect the current behavior of the code. This process is called "regression testing." By creating this test suite before you start refactoring, you create a safety net. After you make changes, you can run the same tests to ensure you haven't broken any existing functionality.
Workflow: Before refactoring a critical BillingService class, run Diffblue Cover on it. It will generate a dozen tests covering various scenarios. Now, perform your refactoring. If all the tests still pass, you can have a high degree of confidence that your changes are safe.
12. Metabob
Legacy code is often plagued not just by simple bugs but by subtle, complex logical errors that have been lurking for years. Metabob is an AI-powered code analysis tool that goes beyond simple syntax errors to detect and help fix these complex problems, such as unhandled edge cases, race conditions, and logical flaws.
Metabob's AI has been trained by analyzing millions of bug fixes from open-source projects, allowing it to recognize problematic patterns that traditional linters often miss. When it finds a problem, it doesn't just flag it; it provides a detailed explanation of why it's a problem and often suggests a context-aware fix. This is invaluable for finding the "silent killers" in a codebase before they cause a production outage.
Example: Metabob might identify a section of code where a resource like a file handle or database connection is not properly closed in an exception path, a common issue in older code that can lead to resource leaks.
13. Kodezi
Kodezi bills itself as an "AI developer assistant," and it's particularly useful for the initial "cleanup" phase of a modernization project. Think of it as the tool you use to clear away the weeds and debris from your archaeological site before you start the real excavation.
It excels at three things: auto-commenting, code optimization, and debugging. You can point it at an entire file of dense, uncommented code, and it will add explanatory comments, making it more readable for the whole team. Its optimization feature can suggest more efficient ways to write loops or handle data structures, and its AI-powered debugger can help pinpoint the root cause of tricky bugs faster than manual methods.
Tip: Before you present a modernization plan to your manager, run a legacy module through Kodezi's auto-commenting and optimization features. The "before" and "after" comparison can be a powerful way to demonstrate the immediate value and improved maintainability your project will deliver.
14. Grip Security
As you modernize, you're not just changing code; you're changing how the system connects to the outside world. Legacy systems are often being broken apart and connected to hundreds of new SaaS applications and APIs. Grip Security is an AI tool that helps you manage the security of these new connections.
Grip's AI automatically discovers every single SaaS application and identity connected to your enterprise, including those created by developers to bridge legacy and modern systems. It maps out these relationships and identifies security risks, such as unused accounts with access to sensitive data or insecure API connections. This is a crucial, forward-looking part of modernization that is often overlooked. It ensures your newly modernized architecture doesn't introduce a whole new class of security risks.
Best Practice: As part of your modernization project's "go-live" checklist, use a tool like Grip Security to scan your environment. It ensures that all the new service accounts, API keys, and integrations you've created are properly secured and accounted for.
The Future of Our Digital Past
The days of treating legacy code as an untouchable black box are numbered. As developers, we're now equipped with an incredible arsenal of AI-powered tools that transform 'code-archaeology' from a daunting chore into an exciting challenge of discovery and renewal. These tools don't replace our expertise; they augment it, allowing us to focus on the complex architectural decisions while they handle the painstaking work of analysis, translation, and validation.
As my colleague Goh Ling Yong often says, the best engineers are not just builders of the new but also responsible stewards of the old. By embracing these AI tools, we can breathe new life into the critical systems that power our world, ensuring they are not just relics of the past but robust foundations for the future.
What are your go-to tools for tackling technical debt and modernizing legacy systems? Did I miss any of your favorites? Share your thoughts and experiences in the comments below!
About the Author
Goh Ling Yong is a content creator and digital strategist sharing insights across various topics. Connect and follow for more content:
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