Struggling with outdated code? AI can help. Legacy systems are costly, insecure, and hard to maintain. Here’s how AI simplifies modernization:
- Faster Code Updates: AI tools analyze and rewrite old code quickly, cutting project timelines by up to 60%.
- Smarter Bug Fixes: AI prioritizes critical issues, saving developers time and reducing security risks.
- Better Documentation: AI generates and updates documentation automatically, making it easier to manage legacy systems.
Key Stats:
- 66% of businesses still rely on decades-old systems.
- AI-powered tools can reduce modernization costs by up to 70%.
- Developers spend 40% of their time fixing bugs – AI can change that.
Want to dive deeper? Keep reading for actionable tips on using AI for code reviews, refactoring, and documentation.
Using AI to Refactor Legacy Code: A Practical Guide with Scott Wierschem
Tip 1: Use AI Tools for Code Analysis and Reviews
AI-driven tools are changing the game for how development teams tackle code analysis and reviews. By using machine learning and natural language processing, these tools can quickly uncover issues that might otherwise take weeks of manual effort to identify [5].
Here’s the reality: developers spend nearly 40% of their time fixing bugs instead of creating new features [6]. On top of that, companies allocate almost 40% of their IT budgets to address problems caused by technical debt [6]. AI-powered tools can significantly cut down on these inefficiencies by catching issues early and prioritizing fixes intelligently. This sets the stage for deeper, AI-supported code reviews.
Running Complete Code Reviews with AI
AI scanners are incredibly effective at identifying performance and security issues. They can pinpoint everything from syntax errors and style inconsistencies to performance bottlenecks and vulnerabilities – all in a single scan [5] [6]. Unlike older static analysis tools that rely on basic rule-based checks, modern AI tools leverage advanced models trained on massive datasets, enabling them to detect complex patterns that often signal bugs or errors [5].
Take Snyk’s DeepCode as an example. This tool uses AI to perform static code analysis, uncovering bugs, vulnerabilities, and areas for improvement in software projects. It doesn’t just flag issues – it provides actionable insights and recommendations, helping teams resolve problems faster [4].
The security advantages are hard to ignore. Over 60% of security breaches stem from vulnerabilities in outdated or unpatched software [6]. AI tools can routinely scan your codebase to catch these vulnerabilities, including outdated libraries, unpatched dependencies, and insecure coding practices that might slip past human reviewers.
Another strength of AI tools is their ability to handle large and complex codebases. They work seamlessly across large-scale projects and environments with multiple programming languages, offering consistent feedback [5]. While AI tools can identify issues at scale, developers still play a critical role in validating and refining these findings. For the best results, consider integrating AI tools into your CI/CD pipeline to get real-time feedback during development [5].
Deciding Which Updates to Do First
Once issues are identified, the next step is prioritizing which fixes to tackle first. AI tools can help by ranking problems based on their severity [8], ensuring your team focuses on the most critical issues.
This prioritization process considers factors like potential impact, exploitability, and the effort required to implement a fix. For instance, an AI tool might flag both a deprecated function and a SQL injection vulnerability, but it will clearly prioritize the SQL injection as the more urgent issue.
SonarQube is a great example of this approach. It identifies code smells, bugs, and security vulnerabilities directly within your IDE, making it easier to address pressing concerns [8]. Combining AI’s prioritization with human oversight ensures that recommendations are both accurate and aligned with your broader development goals [7].
The ultimate goal is to blend the speed and efficiency of AI with the judgment and expertise of human developers. AI tools aren’t meant to replace human reviewers – they’re there to enhance the process. Let AI handle the heavy lifting of scanning and prioritizing, while your team focuses on validating and refining the results. With this collaborative approach, you’ll be ready to move on to the next step: automating code refactoring for even greater efficiency.
Tip 2: Let AI Handle Code Refactoring Tasks
Refactoring legacy code can be a major drain on resources, often eating up 10–20% of project budgets, while over 70% of IT funds are spent on maintenance[9]. AI-driven tools can take on much of this workload, significantly reducing costs and effort.
Today’s AI tools are highly effective at spotting flawed logic and outdated dependencies throughout your codebase[9]. They not only identify issues but also suggest improvements, and in many cases, they can implement fixes automatically – all while ensuring your code’s functionality remains intact.
By automating these processes, AI tools can cut code audit time by as much as 60%[9]. This means your development team can shift their focus to creating new features rather than being bogged down by legacy code. Plus, these tools make syntax modernization a smoother process.
Using AI to Update Code Syntax
AI-powered tools are especially useful for modernizing outdated syntax and even converting code between programming languages or frameworks. Whether it’s a simple syntax update or a full-scale framework migration, these tools can handle the task in hours – what might otherwise take developers weeks to complete manually.
For example, a fintech startup used AI to migrate from Java Spring to Node.js, slashing development time by 40%[9]. Similarly, a healthcare SaaS company revamped its aging PHP codebase with AI tools, improving both stability and efficiency[9]. These updates not only clean up legacy code but also make it more adaptable for integration with modern systems.
AI tools can handle a wide range of tasks, from converting jQuery to ES6 to migrating from Python 2 to Python 3[11][13]. They also ensure consistent styling and improve maintainability. For maximum impact, consider integrating these tools into your CI/CD pipelines to maintain quality continuously[10].
Testing AI Code Changes
Once AI tools have updated your code, thorough testing is essential to ensure the changes work as intended and don’t introduce new issues. Rigorous validation helps maintain functionality and catch potential bugs early.
AI-assisted testing can significantly streamline this process. It has been shown to reduce overall testing time by 35%, improve quality by 20%, and cut costs by up to 40%[15].
For instance, a SaaS provider in 2024 used AI-driven testing to automatically generate unit and integration tests for its microservices. This approach achieved 90% test coverage in just a few hours, reducing bugs by 30% and speeding up the release cycle by 40%[15]. Similarly, a retail app implemented AI-generated regression testing, cutting test creation time by 60% and boosting coverage by 25%[15].
Despite these advancements, human oversight remains vital. Developers should review AI-generated tests to ensure they include strong assertions, cover all requirements, and account for edge cases[14]. Static analysis tools can further help by scanning for bugs and security vulnerabilities. Think of AI-generated tests as a starting point that can be refined through manual review.
To achieve the best results, refactor in small increments, maintain robust test coverage, and use version control for easy rollbacks[12]. Combining AI insights with human expertise ensures a well-rounded approach, and integrating AI testing into CI/CD pipelines provides continuous validation[15].
Build Your Dream Software
Partner with OpenArc for custom software development solutions. Let us help you create innovative software tailored to your needs. Ready to discuss your software modernization project?
Tip 3: Create Better Documentation with AI
Outdated or incomplete documentation can make legacy system modernization a headache, leaving developers to decipher cryptic or missing comments. AI can step in to simplify this process by generating detailed documentation and keeping it updated as your codebase evolves.
Modern AI tools are capable of analyzing your code, understanding its functionality, and creating clear, precise documentation [17]. This is especially helpful for legacy systems, where original documentation may be missing or insufficient. Instead of spending hours manually documenting thousands of lines of code, you can focus on enhancing your systems strategically.
Auto-Generating Documentation from Code
AI tools can automatically generate comprehensive API documentation, identify missing comments, create error-free code examples, and improve readability with formatting [17]. These capabilities are a game-changer for legacy codebases.
Take DocuWriter.ai, for example – it reportedly saved developers 87,500 hours that would have been spent manually writing documentation [16]. By starting with legacy code, which often contains extensive data, AI tools can produce highly accurate and detailed results [18].
“With generative AI modeling techniques, the documentation process would become progressively seamless and automated whether an application is being documented for the first time or its documentation is being updated.”
– Miten Marfatia, founder and CEO, EvolveWare [18]
This automated documentation provides a solid foundation for keeping your records accurate as your code evolves.
Keeping Documentation Current
Creating documentation is only part of the equation – keeping it up to date is just as important. AI can help ensure that your documentation evolves alongside your code. It can identify outdated content, update documentation with recent changes [16], and flag inconsistencies [21]. By integrating AI into your CI/CD pipelines, you can prevent documentation from drifting out of sync with your codebase.
For example, GitLab AI can generate release notes and update API documentation based on the latest commits, significantly reducing manual effort [21]. Additionally, AI tools can analyze documentation for readability and tone, ensuring technical terms are used consistently and accurately [20]. This enhances team collaboration by providing a reliable, up-to-date knowledge base, speeding up onboarding, and reducing miscommunication [17].
“Generative AI can certainly be used to streamline documentation processes. It’s particularly promising and valuable in that it can help accelerate the process of keeping legacy content fresh and accurate.”
– Sanjay Sarathy, vice president, marketing, Cloudinary [18]
That said, human oversight remains critical. AI is a powerful assistant, but it can’t replace human judgment. Developers should review AI-generated documentation to ensure it aligns with organizational standards and is easy to understand. Establishing a consistent document structure and feedback channels will further improve the quality and usability of your documentation [19].
Conclusion: Creating Code That Lasts
AI-powered modernization offers a smarter way to update legacy systems, focusing on incremental, strategic improvements rather than risky, large-scale rewrites. This marks a major shift in how organizations maintain and enhance their software assets, paving the way for more efficient and manageable system evolution.
Currently, organizations allocate nearly 40% of their IT budgets to resolving issues caused by technical debt, while developers spend a similar portion of their time fixing bugs instead of building new features [6]. AI-driven tools have shown impressive results, increasing efficiency by 30–60% through automated deep-code analysis, with accuracy rates of 85–90% when converting outdated languages into modern architectures [1].
But the benefits go beyond just performance boosts. AI actively safeguards code quality by providing real-time system monitoring during migrations. It identifies potential issues early and suggests fixes before they escalate [3]. This proactive approach, combined with automated analysis and refactoring, ensures long-term code integrity. Unlike traditional methods that rely on limited expert resources, AI can analyze millions of lines of code tirelessly, reducing the risk of errors while speeding up modernization [1].
The most effective modernization strategies embrace a continuous modernization mindset, integrating updates into the regular development cycle rather than treating them as isolated projects [2]. This approach transforms static legacy code from a costly liability into a dynamic, evolving asset that aligns with your business goals.
That said, human oversight remains crucial. AI tools are designed to assist, not replace, developers. For complex logic or project-specific requirements, having developers review and validate AI-generated code ensures accuracy and adaptability [23]. Striking this balance between AI automation and human expertise not only creates robust, secure code but also helps organizations build systems that stand the test of time. By combining AI’s strengths with thoughtful human input, businesses can modernize confidently and effectively.
FAQs
How can AI tools help identify and prioritize bug fixes in an outdated codebase?
How AI Tools Simplify Bug Fixes in Legacy Codebases
AI tools have become a game-changer for tackling bugs in legacy codebases. With advanced algorithms at their core, these tools can automatically identify bugs, spot vulnerabilities, and flag outdated code patterns. This means developers can skip the tedious manual searches and focus on solving the real issues. Plus, by analyzing historical data, AI can detect recurring trends, predict problem areas, and even recommend fixes based on patterns it’s learned from extensive datasets.
AI also takes the hassle out of bug triage. It can rank issues by their potential impact, ensuring that the most critical problems are addressed first. This prioritization helps teams zero in on what truly matters, boosting the codebase’s quality, performance, and maintainability – all while lightening the manual workload for developers.
How does integrating AI into the CI/CD pipeline help with modernizing code?
Integrating AI into your CI/CD pipeline can transform how you tackle modernizing your code. By automating tasks like code reviews and testing, AI not only speeds up the process but also helps minimize human errors. It can identify outdated code patterns, recommend improvements, and even predict potential deployment risks, allowing you to address them before they become issues.
AI also strengthens team collaboration by creating faster feedback loops and improving how resources are managed. The result? Better code quality, quicker delivery times, and a codebase that’s easier to scale and maintain for future projects.
How can AI help keep documentation for legacy systems accurate and up-to-date?
AI streamlines the process of keeping documentation for legacy systems accurate and current by automating the tracking and recording of code changes. Whenever updates are made, AI tools can evaluate those changes, identify dependencies, and produce updated documentation that mirrors the latest state of the codebase.
This approach cuts down on manual work, reduces the likelihood of errors, and keeps documentation aligned with the system’s ongoing development. By incorporating AI, teams can dedicate more time to high-priority tasks while ensuring their technical documentation remains dependable and up-to-date.