10 AI Code Refactoring Tools for Legacy Systems 2024

Here's what you need to know about fixing old code with AI in 2024:

Tool	Best For	Key Feature
Laminar	Legacy Integration	No-code system connections
GitHub Copilot	Code Updates	Real-time suggestions
CodeWhisperer	AWS Migration	AWS-specific code help
SonarQube	Code Analysis	One-click fixes
Sourcery	Python Code	Quality scoring
Bloop	COBOL to Java	Code conversion
CodeSee	Visual Mapping	Code relationship maps
IntelliJ IDEA	Java/Kotlin	Smart refactoring
ReSharper	.NET Code	Deep cleanup
CodeRush	Visual Studio	100+ refactoring options

Here's the thing: 45 states still run on COBOL mainframes. During COVID-19, we saw what happens when these old systems break - unemployment systems crashed nationwide.

But there's good news: 92% of developers now use AI tools to fix legacy code. These tools can:

What They Do	Why It Matters
Find weak spots	Stop crashes before they happen
Update safely	Keep systems running while fixing
Write docs	Make code easier to maintain
Break down apps	Turn big systems into smaller pieces
Clean up code	Make updates faster

The numbers back this up: Teams using AI tools fix code 55.8% faster and catch bugs 85% of the time (vs. 60% manually).

Warning: Don't try fixing everything at once. Let AI help you improve bit by bit, testing as you go.

Related video from YouTube

What is AI Code Refactoring?

AI code refactoring uses machine learning to clean up old code without breaking its functionality. It's like giving your codebase a makeover - same function, better form.

Here's what AI does during refactoring:

Task	How AI Helps
Code Analysis	Finds issues and offers quick fixes
Documentation	Writes clear docs based on existing code
Structure Improvement	Makes complex code simpler
Testing	Makes sure everything still works
Migration	Updates code to newer frameworks

Let's talk numbers: Developers spend 75% of their time fixing and maintaining code instead of building new stuff. IBM found that 83% of IT leaders say they NEED to update their old apps.

But there's a catch: 63% of companies skip code cleanup. Why? They think it takes too long. This creates problems:

Problem	Result
Messy Code	Bug fixes take forever
Poor Performance	Everything runs slow
Higher Costs	Maintenance eats the budget
Security Risks	Harder to fix security holes

Want proof it works? Check this out:

Deloitte tested AI code tools and saw a 20% speed boost in development. Developers gave the AI's code fixes a thumbs up, rating them 65%+ accurate.

GitHub Copilot users crushed their tasks 55.8% faster than teams working without AI. The tool helps:

• Fix confusing code
• Break up big functions
• Pick better names
• Group related code

Here's the smart way to use it: Don't try to fix everything at once. Let AI help you improve your code bit by bit, keeping everything working while you do it.

Laminar

Laminar is an AI platform that helps teams fix and connect old systems. It's built for industries like logistics, oil and gas, manufacturing, and retail.

Here's what it does:

Feature	Benefit
No-Code Integration	Connect systems without coding
AI Workflow Generation	Creates system connections automatically
Legacy System Support	Handles mainframes, ERPs, and CRMs
Built-in Monitoring	Spots and flags problems instantly

The platform speeds up system updates in two ways:

Time Saving Area	How It Works
Initial Setup	AI scans your systems and shows where to connect them
Maintenance	Tools catch and fix issues early

Pick the plan that fits your team:

Plan	Monthly Cost	Integration Limit
Starter	$4,999	10 integrations (600 workflows)
Scale	$14,999	50 integrations (3000 workflows)
Enterprise	Custom	Unlimited + custom deployment

Every plan comes with error alerts and support. Start small, grow when you need to.

Here's the thing:

If you're stuck with old systems, Laminar lets you update them without the headaches of a complete rewrite. Your team can handle updates in-house instead of paying consultants for quick fixes.

It's perfect if you:

• Want to link old and new systems
• Need to cut maintenance costs
• Have multiple old systems to update
• Can't afford system downtime

Your engineers can test everything through the web API before going live. Simple as that.

2. GitHub Copilot

GitHub Copilot is an AI coding assistant that helps modernize old code. Built through a partnership between GitHub and OpenAI, it uses GPT-3 to analyze and improve existing codebases.

Here's what it does:

Feature	How It Helps
Code Analysis	Breaks down complex code and explains its function
Smart Comments	Writes clear documentation for code sections
Code Cleanup	Identifies outdated code patterns
Code Updates	Recommends modern coding practices
Test Creation	Builds basic tests for existing functions

Let's see it in action. Here's how Copilot transforms old Python code:

# Before
result = []
for number in numbers:
    result.append(number * number)
return result

# After
return [number ** 2 for number in numbers]

Copilot focuses on three main areas:

Area	Benefit	Example
Code Updates	Makes code current	Updates Python 2 to 3
Bug Fixes	Improves code safety	Adds null checks
Better Patterns	Shows modern methods	Uses list comprehensions

What It Costs:

Plan	Cost/Month	Features
Individual	$10	Basic AI help
Business	$19	Team tools + security
Enterprise	Custom	Extra security + support

Watch Out For:

• Always check AI suggestions
• May miss edge cases
• Best with standard code

Think of Copilot as your first draft editor. Let it spot potential improvements, but always review its suggestions against your coding standards. It's here to speed up your work, not replace your judgment.

3. Amazon CodeWhisperer

Amazon CodeWhisperer is an AI code assistant that gives you real-time suggestions to modernize your legacy code. It plugs right into popular IDEs like VS Code and JetBrains.

Feature	Description
Language Support	Java, Python, JavaScript, TypeScript, C#, Go, PHP, Rust, Kotlin, SQL, and more
Security Scanning	Spots exposed credentials and log injection risks
AWS Integration	Built-in support for AWS services and APIs
IDE Support	Works with VS Code, JetBrains IDEs, AWS Cloud9

Here's what CodeWhisperer does with your old code:

Task	What It Does
Code Analysis	Spots old patterns and suggests modern updates
AWS Migration	Converts code to work with AWS services
Security Fixes	Finds and patches old security issues
Documentation	Makes old code easier to understand with clear comments

Let's see CodeWhisperer in action with some DynamoDB code:

// Old way
public void getItem(String tableName) {
    Map<String, AttributeValue> key = new HashMap<>();
    key.put("id", new AttributeValue().withS("123"));
    GetItemRequest request = new GetItemRequest()
        .withTableName(tableName)
        .withKey(key);
    GetItemResult result = ddb.getItem(request);
}

// CodeWhisperer suggestion
public void getItem(DynamoDbClient ddb, String tableName) {
    try {
        String result = ddb.getItem(request -> request
            .tableName(tableName)
            .key(key))
            .item()
            .get("name")
            .s();
        System.out.println("GetItem succeeded: " + result);
    } catch (DynamoDbException e) {
        System.err.println(e.getMessage());
    }
}

Plan	Cost	What You Get
Individual	Free	Basic suggestions with AWS Builder ID
Professional	Custom	Team features + admin tools

Quick Tips:

• Write clear comments to get better suggestions
• Run security checks before using suggestions
• Look for AWS-specific tips
• Test all generated code before going live

If you're moving old systems to AWS, CodeWhisperer is your best friend - it knows AWS inside and out.

4. SonarQube

SonarQube now packs AI-powered code analysis for legacy system updates. Their AI CodeFix feature spots and fixes code problems in Java, JavaScript/TypeScript, C#, Python, and C/C++.

Feature	Description
AI CodeFix	Fixes bugs, security issues, and code problems with one click
Code Analysis	Spots bugs, weak points, and messy code in real-time
Quality Gates	Sets rules your projects must follow before release
Language Support	Handles 30+ programming languages
CI/CD Integration	Fits right into your DevOps pipeline

Here's what SonarQube finds in your code:

Issue Type	What It Catches
Bugs	Code that breaks at runtime or has logic flaws
Security	Places where data might leak or get attacked
Code Smells	Code that's hard to update or maintain
Duplications	Copy-paste code that needs cleanup
Test Coverage	Code that needs more testing

The Numbers Tell the Story:

Check out these results from teams using SonarQube:

Metric	Result
Code Maintenance	Cut time by 40%
Security Risks	Dropped by 90%
Software Lifespan	Added 3 more years
Code Coverage	Jumped from 71% to 89% (July '22 - March '23)
Code Smells	Down from 381 to 355 (Aug '22 - May '23)

How SonarQube Does Its Magic:

1. Scans your code non-stop while you work
2. Catches problems before they hit production
3. Gives you AI-powered fixes
4. Stops releases that don't meet quality rules

For legacy systems, you can set rules like:

• No show-stopping issues
• 80%+ code coverage on new code
• No exposed passwords or keys
• Zero big security holes

Want the best results? Run SonarQube all the time and fix issues as they pop up. This keeps tech debt low and makes your legacy code better day by day.

5. Sourcery

Sourcery is an AI coding assistant that helps Python teams fix and maintain their code. It spots problems and shows solutions directly in your code editor.

Feature	Description
Code Quality Scoring	Gives each function a 0-100% score based on size, complexity, and memory use
IDE Support	Plugs into VS Code, PyCharm, Sublime, and Vim
AI Code Review	Checks pull requests and comments on specific lines
Test Generation	Builds unit tests for your code
Docstring Creation	Writes function documentation

Here's what Sourcery delivers:

Benefit	Impact
Time Savings	25% less time spent on old code
More Features	Teams ship 2x more features when code is clean
Less Manual Work	AI handles repetitive code fixes
Security	Deletes code after 30 days, doesn't use it for training

How It Fixes Old Code:

Function	What It Does
Code Extraction	Spots duplicate code you can turn into methods
List Optimization	Makes loops shorter with list comprehensions
Variable Placement	Puts variables where you first need them
Problem Detection	Finds extra exception handlers

"Sourcery will understand the code as he's writing it, and offer those refactorings suggestions in real time." - Brendan Maginnis, CEO at Sourcery

Tips to Get Started:

• Write # sourcery skip to ignore suggestions
• Hit Alt+Enter to use a suggestion
• Switch to Code Review to see branch differences
• Make rules that match your team's code style

Sourcery lets teams build new stuff instead of fixing old code. Use it free on public repos, or try Pro/Team plans for private projects (14-day trial included).

6. Bloop

Bloop combines GPT-4 with code search to help teams turn COBOL into Java. It's built to handle big, old codebases without making a mess.

Here's what Bloop does:

Feature	Description
Code Conversion	Turns COBOL into clean Java code
AI Testing	Makes sure Java code works like the COBOL version
Multi-Repo Search	Connects to your Git repos
Simple Questions	Search code by typing normal questions
Local Processing	Works offline for better security

The tool packs some serious power:

Function	What It Does
Smart Search	Finds code using GPT-4's brain
Code Updates	Makes old code new (and keeps it working)
Code Reading	Helps you get what the code does
GitHub Ready	Works with your GitHub stuff
Test Creation	Makes tests that actually help

Want to use it? Here's how:

Step	What To Do
Get Started	Connect GitHub and pick your repos
Look Around	Ask questions about your code
Check Work	Make sure the new code fits your needs
Test It Out	Run the AI tests to check everything
Ship It	Update code without stopping work

To get the most from Bloop:

• Keep your questions clear and specific
• Connect your repos before searching
• Double-check what the AI writes
• Test everything (seriously, everything)

If you're stuck with old systems, Bloop's got your back. It converts code directly - no cleanup needed after. Perfect when time's tight but you need good code.

What it costs:

Plan	What's Included
Free	Basic search and analysis
Premium	Full conversion + testing
Team	Everything for multiple users

Bloop shines when dealing with messy old code. It spits out clean, modern code that won't make future developers cry.

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7. CodeSee

CodeSee is a visual code mapping tool that connects to GitHub repos. It shows you exactly how different parts of your codebase work together.

Here's what you get with CodeSee:

Feature	What It Does
Visual Code Maps	Shows how files and services connect
AI-Powered Search	Ask about your code in plain English
PR Review Maps	View code changes and their effects
Code Automations	Lock down code during refactoring
Cross-Repo Views	See dependencies across repos

The AI helps you work smarter:

Task	Benefit
Split Big Systems	Break monoliths into services
Find Links	Spot hidden code connections
Plan Better	Know which changes are safe
Stay Updated	Keep maps in sync with code
Help New Devs	Share code knowledge fast

Here's what teams get from CodeSee:

Metric	Impact
Time Saved	4 hours less on code reviews weekly
Code Understanding	60% faster code reading
Team Onboarding	New devs get up to speed faster

"CodeSee's automated cross-repo visualization tools allow our development teams to see inside each other's codebases. Our devs use CodeSee's holistic view of our system to more easily understand their codebase and to communicate the complexity of their work to other people around the business." - Chris Poel, Head of Engineering, River Island

"Using CodeSee to take on a new codebase, I'm able to save up to 4 hours a week across my open-source projects." - Ryan Abrams, Senior Engineer, Stripe

Get the most from CodeSee by:

• Adding visual guides to PRs
• Setting up code standard checks
• Using AI for code questions
• Mapping links before changes
• Sharing maps with your team

The new GPT features let you ask things like "how does logging work?" or "how is state management used?" You'll get text answers AND visual maps showing the connections.

Plan Type	Features
Basic	Code maps, PR reviews
Enterprise	Full refactoring tools, AI features
Beta Access	New generative AI tools

Built on Microsoft and OpenAI tech, CodeSee helps teams tackle old code problems. It keeps your code private - analyzing without storing it elsewhere.

8. IntelliJ IDEA

IntelliJ IDEA's AI Assistant makes fixing old code easier. Here's what you get:

Feature	Description
AI Actions	Fix code with a right-click - explains, suggests changes, spots issues
Code Analysis	Looks at how code pieces connect and work together
Test Generation	Makes unit tests for your code
Language Conversion	Switches code between languages (like Java to Kotlin)
Custom Prompts	Save your go-to questions for quick access

Here's how to use it:

Step	Action
1. Select Code	Pick the code you want to fix
2. Open AI Menu	Hit ⌥⏎ (Mac) or Alt+Enter (Windows/Linux)
3. Choose Action	Click "AI Actions > Suggest Refactoring"
4. Review Changes	Look at what the AI wants to change
5. Apply/Modify	Say yes to changes or tweak them

The AI helps clean up old code by:

Task	How It Works
Algorithm Analysis	Makes complex code easier to understand
Name Updates	Picks better names for variables and methods
Code Patterns	Spots old coding styles you should update
Problem Detection	Finds bugs and issues
Documentation	Explains what the code does

What's new in 2024.1:

New Feature	Function
Full Line Completion	Guesses whole lines of code
OpenRewrite Integration	More ways to fix code
Cloud Code Completion	Smarter suggestions using cloud AI

Quick Tips:

• Run tests after each change
• Modify existing prompts instead of making new ones
• Check the diff viewer before saying yes
• Set up prompts for tasks you do often

Works with any type of code - perfect for updating old systems. You'll need a JetBrains AI subscription to use all features.

9. ReSharper

ReSharper's 2024.2 update adds AI features to fix .NET legacy code faster. Here's what's new:

Feature	What It Does
AI Assistant	Explains code, finds problems, writes XML docs
Code Cleanup Profiles	3 preset options to clean code
Solution-wide Analysis	Spots dead code and issues across projects
Predictive Debugger	Shows code outcomes without running it
Async Typing	Works outside VS for speed

The cleanup profiles make it simple:

Profile	Purpose
Reformat Code	Fixes basic formatting
Reformat & Syntax Style	Makes code match standards
Full Cleanup	Deep code cleanup

The AI gets smarter in 2024.2:

Feature	Function
In-editor Suggestions	AI helps as you type
Exception Explanations	Makes errors clear
C# 13 Support	Handles latest C# features
GPT-4 Integration	Better code analysis

Setting up is fast:

1. Install: Get ReSharper
2. Turn on: Enable code analysis
3. Choose: Pick a cleanup profile
4. Clean: Hit Ctrl+E+C

The price drops each year:

• Year 1: $129
• Year 2: $103
• Year 3+: $77

"Clean, consistent code saves me 15-30 minutes every day and cuts out tons of headaches." - Scott Lilly, Author at ScottLilly.com

Make it work better:

• Share your cleanup settings
• Clean code when saving
• Sort issues by type
• Turn on full solution analysis

It works with Visual Studio and all .NET versions, including .NET 9 Preview. Want the AI Assistant? Install it separately - but you might need to wait in line.

10. CodeRush

CodeRush by DevExpress is a Visual Studio extension that fixes legacy code using AI. Here's what you get in 2024:

Feature Type	Capabilities
Code Analysis	- Spots code smells and anti-patterns - Finds duplicate code - Checks spelling automatically
Refactoring Tools	- 100+ refactoring options - One-click API updates - Legacy code modernization
AI Assistance	- Code generation - Pattern detection - Style improvements
Navigation	- Go to Definition - Find Usages - Position markers

What makes it stand out:

Aspect	Implementation
Interface	Inline editing vs dialog boxes
Speed	Faster test runner for .NET
Compatibility	VS 2015-2022 support
Cost	$249 one-time payment

When you're dealing with legacy systems, you'll love these features:

Feature	Purpose
Template Library	Reduces code duplication
Next-gen Debugger	Shows runtime issues
Context Help	Explains complex code
Parallel Refactoring	Updates multiple files

"It's polished... The graphics are JAW DROPPING. You literally have to see it to believe it." - Scott Hanselman, Microsoft Employee

Get more from CodeRush:

Tip	Result
Create shortcut sheet	Access 100+ features fast
Use inline editing	Quick code fixes
Enable spell check	Catch naming issues
Set up markers	Track editing spots

CodeRush supports C#, Visual Basic, and XAML. You can try it free on Visual Studio 2015-2022, or unlock all features for $249. The 2024 version packs more AI power for handling legacy code.

How to Use These Tools Effectively

Here's how to maximize AI code refactoring tools in 2024:

Phase	Key Actions	Success Metrics
Setup	- Set up version control - Create test suites - Define coding standards	- Test coverage % - Code quality scores - Build success rate
Implementation	- Make small changes - Run tests after each change - Document modifications	- Defect reduction - Time saved - Code maintainability
Validation	- Security scans - Performance testing - Code reviews	- Security scores - Response times - Review completion %

The numbers don't lie: McKinsey's 2024 study shows developers code 2x faster with AI tools. But there's more to it than speed.

Here's what you need to test:

Testing Type	Purpose	Frequency
Unit Tests	Check individual components	After each change
Integration Tests	Verify system connections	Daily
Security Scans	Find vulnerabilities	Weekly
Performance Tests	Monitor speed and resources	Bi-weekly

"While AI brings power to coding, we must keep sharpening our programming skills and stick to security basics." - Prasanna Dasari, IT Leader

Want results like the 33% productivity boost shown in the Stackoverflow survey? Here's how:

Best Practice	Implementation Steps	Expected Outcome
Code Reviews	- Set review guidelines - Use automated checks - Track metrics	60-70% fewer defects
Documentation	- Update docs immediately - Include AI usage notes - Add code comments	Better maintenance
Security	- Run SAST analysis - Check dependencies - Monitor alerts	Reduced vulnerabilities

For older systems:

Focus Area	Action Items	Tools to Use
Dependencies	- Isolate external calls - Create interfaces - Update libraries	SonarQube, Bloop
Performance	- Monitor metrics - Check memory usage - Track CPU load	CodeRush, ReSharper
Testing	- Add unit tests - Create mocks - Set up CI/CD	GitHub Copilot, IntelliJ

AI tools spot issues 30-50% faster than manual reviews. But they're not magic - test everything and keep security front and center.

What's Next for AI Code Tools

AI code tools are changing how we build software in 2024. Here's what's happening:

Feature	Expected Timeline	Impact
Full Task Automation	Q2 2024	GitHub Copilot Workspace handles end-to-end dev tasks
Legacy System Analysis	Q3 2024	AI maps system connections and splits into microservices
Cross-Language Translation	Q4 2024	Code converts between languages automatically

The biggest change? More non-developers are writing code:

Role	AI Tools Used	Main Tasks
Product Managers	Code Assistants	Building simple features
Business Analysts	GenAI Tools	Creating data reports
System Admins	AI Refactoring	Updating infrastructure

"AI assistants cut down boring tasks, but they're making it harder to test new hires' skills." - Ty Dunn, CEO and co-founder of Continue

Let's look at the numbers:

Metric	Current	2024 Projection
CXOs Prioritizing Legacy Updates	70%	85%
Enterprises Using Legacy Systems	66%	40%
Customer-Facing Legacy Apps	60%	35%

Here are the three big shifts coming:

1. Code Generation Systems

Developers won't just write code - they'll build systems that write code. Think better prompts and smarter AI models.

2. Refactoring Focus

Teams need new skills to clean up AI-generated code. Regular code reviews will focus on making things simpler and clearer.

3. Documentation Evolution

AI will write clear comments for old code, making it easier to work with legacy systems.

"AI tools are moving past the basic stuff." - Steven Schkolne, CEO and founder of MightyMeld

For legacy system developers, here's what's changing:

Area	Current AI Capability	Next Evolution
Code Analysis	Spots basic patterns	Maps complex connections
Testing	Makes unit tests	Builds complete test suites
Security	Basic security checks	Spots future threats
Documentation	Writes comments	Creates system blueprints

The game is changing from writing new code to improving what's already there. Less time fixing bugs, more time making systems better.

Summary

Here's a look at AI code refactoring tools in 2024:

Tool Type	Best For	Main Benefits
Code Assistants	Daily Development	Spots errors, suggests fixes
Analysis Tools	Legacy Systems	Maps dependencies, finds issues
IDE Extensions	Team Projects	Real-time suggestions, automated fixes
Custom Solutions	Enterprise	Legacy system integration, workflow automation

The numbers speak for themselves:

Metric	Before AI Tools	After AI Tools
Code Review Time	4-5 hours/week	1-2 hours/week
Bug Detection	60% accuracy	85% accuracy
Refactoring Speed	3-4 days/module	1 day/module

What to look for in a tool:

Factor	What to Check	Why It Matters
Code Base Size	Tool scaling limits	Affects processing speed
Language Support	Supported versions	Must match your stack
Integration Options	API access	Connects with your tools
Security Features	Code scanning	Protects sensitive data

Smart practices for AI tools:

Do	Don't
Test small changes first	Refactor everything at once
Keep test coverage high	Skip manual code reviews
Document AI decisions	Rely only on AI suggestions
Monitor performance	Ignore security warnings

Tools for different systems:

System Type	Best Tool Choice	Main Task
Mainframes	Laminar	API integration
ERPs	SonarQube	Code analysis
CRMs	GitHub Copilot	Code updates
Custom Apps	CodeWhisperer	Modernization

4-step implementation:

Step	Action	Result
1	Set clear goals	Focused improvements
2	Start small	Quick wins
3	Check results	Better code quality
4	Share knowledge	Team learning

The bottom line:

Area	Impact
Code Quality	40% fewer bugs
Team Speed	3x faster updates
Cost Savings	50% less maintenance

These tools boost productivity and cut down errors. But here's the thing: they work best when paired with human judgment and specific objectives.

FAQs

How AI can improve code performance?

Here's how AI makes your code better:

Area	Impact	Details
Time Savings	70% reduction	Speeds up code understanding that eats up 70% of dev time
Code Analysis	24/7 monitoring	Spots problems and offers fixes automatically
Success Rates	30-37%	PaLM 2 (37.29%) and GPT-3.5 (30.26%) show solid results fixing code
Testing	Pre/post checks	Catches bugs early, before they hit production

AI's biggest strengths:

Task	How It Works	Results
Pattern Detection	Looks for common code issues	Spots things developers might skip
Code Structure	Points out better ways to organize	Makes updates easier later
Performance Fixes	Finds slow spots in code	Makes your code run faster
Standards Check	Makes sure code follows rules	Keeps everything consistent

But AI isn't perfect:

Limitation	What To Do
Doesn't get business needs	Keep developers involved
Misses complex details	Double-check AI's work
Results change by task	Test changes bit by bit
Some fixes don't fit	Use AI as a helper tool

To get the most from AI:

• Write your tests first
• Look over AI's suggestions
• Make small changes
• Mix AI tools with human know-how

Bottom line: AI speeds things up, but it needs human oversight. Let AI find issues and suggest fixes - then let developers make the smart calls about what to use.

Related posts

• AI-Powered Legacy Code Refactoring: Guide 2024
• Generative AI for Legacy Code Modernization: Guide
• 5 Ways Generative AI Modernizes Legacy Systems
• Generative AI for Legacy System Modernization: Myths Debunked