Reviving Legacy Code: How AI Transforms Code Debt into a Strategic Advantage

Imagine a once-great corporate headquarters where old software makes everything slow and difficult. Employees log in daily but face frustration — tasks take longer to complete, security risks increase, and finding valid data feels like solving a complex puzzle.

Old software slows down work and increases security risks for businesses. Legacy code costs money and stops innovation.

This was StratusNex’s reality: a monolithic Java system built in the early 2000s, with a codebase that looked like a labyrinth of tangled logic and forgotten patches.

While competitors raced ahead with AI-driven solutions, StratusNex’s team spent their days propping up a digital relic.

But this case is more about survival, not just old code.

Innovations create new opportunities but can also lead to problems, like legacy code debt.

According to McKinsey, 70% of organizations struggle with code debt, spending 20-40% of their IT budget just to fix it.

These staggering numbers show the urgent need for effective ways to reduce code debt.

We will look at practical steps and valuable insights organizations can use to cut costs and make software more sustainable.

Key takeaways

• Organizations spend 20-40% of their IT budgets fixing issues with old technology, and outdated systems increase data breach costs by 10% annually.
• AI tools like GitHub Copilot reduce manual coding by 70% and automate security updates for old frameworks like Apache Struts.
• Success with AI depends on training developers in AI tools, creating governance frameworks like Centers of Excellence, and integrating AI into CI/CD pipelines.
• JetRuby’s AI approach cut migration costs by 70% from €1.2 million to €360,000, reduced downtime, and increased onboarding by 40%.

What’s legacy code?

Legacy code refers to an existing code base passed down from previous teams. It can be functional, but it’s challenging to manage due to modifications by various developers and the potential use of outdated technologies.

Many companies keep using legacy code because it feels familiar, even if it’s not efficient.

Rewriting it can be risky, expensive, and disruptive, so teams often choose to maintain it instead. While this avoids major changes in the short term, it can still be hard to update, especially when there’s little documentation or new team members need training.

Problems legacy code can cause

Legacy code can cause many problems for organizations that choose to keep it. It often doesn’t work well with new technology, which can hurt product performance and growth, and affect customers’ experience of the product in the future.

Legacy code often lacks modern security tools, such as automated scanners and static analyzers. Without them, developers struggle to detect issues when they update the code.

When they do not fully understand the codebase, new security gaps can appear.

The financial impact is clear.

IBM’s 2024 Cost of a Data Breach Report reveals that the cost of legacy systems is around $4.88 million (a massive 10% increase over last year and the highest total ever!)

The U.S. Cybersecurity and Infrastructure Security Agency warns that using outdated software for critical operations can be risky. It can threaten national security, the economy, and public health.

No industry is immune, and reliance on old code brings both financial and operational risks.

How AI can help with legacy code

AI speeds up software development and can help teams update old code more effectively.

Tools like GitLab Duo explain existing code and create new code, making it easier to modernize systems. AI also assists with testing and security by identifying root causes, generating tests, and fixing vulnerabilities.

Research shows that 34% of AI users in software development are modernizing legacy code, reaching 46% in financial services.

However, AI is not perfect and needs testing, clear guidelines, and human oversight. It should be used alongside other security tools.

We recommend creating a dynamic software bill of materials (SBOM) to understand licensing and security risks related to your software, including older code.

JetRuby created a program that helped the client’s developers use Copilot confidently to update Apache Struts. The program included strong security measures and good performance practices and worked across 19 applications.

This approach reduced manual work by 70%, saved costs, and led to quick and lasting use.

Training the Team: Building In-House AI Skills for Company Growth

We carefully structured our training to shift the client’s developers from bystanders into hands-on Copilot practitioners.

We focused on three key areas.

Mastering Contextual Prompt Engineering

We held workshops where participants learned how to make clear and specific requests. For example, instead of just asking Copilot to change old tags, engineers explained how to convert a Struts 1.x loop into a Struts 7 iterator.

They included details like pagination settings and XSS protections so Copilot could replace the tag and add the right library declarations.

In another exercise, teams described how to update an old Action.execute() method to the latest ActionSupport style. They specified using an up-to-date validation interceptor and explained when to skip validation.

This method kept all current business rules while also following modern security standards.

Embedding Struts 7 Security & Performance Best Practices

Our deep-dive sessions addressed the client’s most critical risks:

• Session Management: We guided developers to replace insecure, cookie-only session tracking with encrypted JSON Web Tokens stored in HTTP-only cookies, fully aligning with OWASP recommendations.
• Centralized Input Validation: Instead of scattering checks throughout JSPs, teams implemented a unified validation framework in Struts 7, using regex rules to guard against SQL injection and cross-site scripting.
• Memory Leak Remediation: Participants audited legacy modules to identify and close unbounded JDBC connections, eliminating a chronic source of outages that had accounted for 15% of production downtime over five years.

Scaling Across 19 Applications

After the proof of concept, we rolled out a standardized migration playbook:

• Template Library: We curated over 120 reusable prompt templates, organized by scenario, such as form-tag conversion, session encryption, and dependency injection, and hosted them in a shared repository for on-demand access.
• CI/CD Integration: The team embedded Copilot-driven migrations into their Azure Pipelines via GitHub Actions. The automated workflow scanned for legacy files, applied pre-configured prompts, and deployed updates to a staging environment for automated vulnerability scans.
• Cross-Team Governance: A new Center of Excellence of senior engineers documented tricky edge cases (for example, custom PDF-generation plugins), established a clear decision tree for handling deprecated features, and held biweekly “AI Retrospectives” to circulate lessons learned.

Within six weeks, the client’s in-house staff had independently upgraded four mission-critical systems — order management, billing, CRM, and inventory. It reduced reliance on our consultants by 80% and sustained a 98% compliance rate in automated code audits.

Delivering Quantifiable Efficiency Gains

Legacy code required substantial developer time for updates, but modern tools make the process easier. They helped save hundreds of hours per sprint and maintain most test coverage and high code quality.

70% Reduction in Manual Development Effort

• Before Copilot, each module required roughly 22 hours of manual labor — reworking dozens of JSP files, refactoring numerous Action classes, and performing comprehensive cross-browser testing.
• With Copilot, Tag conversions became almost immediate (about 12 minutes per file), and class refactoring dropped to 25 minutes each. Copilot-generated AI unit tests covered 85% of critical logic versus just half under the old manual process.

For example, a legacy purchase-order module with 68 JSP pages and 19 actions went from 51 hours of work to only 16 hours, saving more than 73 hours (around a 74% reduction) in a single sprint.

Two-Week Proof-of-Concept Timeline

Now, more on how we quickly executed our proof-of-concept (PoC) using strategic planning and AI tools.

In 14 days, we tested the migration framework, moving from the client’s outdated setup to a more sustainable approach while ensuring knowledge transfer. The timeline shows key milestones.

Days 1–2: Environment Setup

Mirrored the client’s Java 8/Struts 1.x production stack in Azure and configured Copilot with project-specific style rules.

Days 3–7: Automated Tag Migration

Converted over 420 legacy tags across dozens of views, validated by regression testing scripts that Copilot generated.

Days 8–10: Action Class Refactoring

Updated 14 core classes to the new framework conventions and produced nearly 60 unit tests that covered more than 90% of the business logic.

Days 11–14: Training & Handoff

Delivered 12 hours of hands-on workshops, published a 45-page migration playbook, and demonstrated how to trigger migrations through the CI/CD pipeline.

Long-Term Organizational Impact

We are using AI to make development processes more effective. Innovative tools help us and our clients get better results.

Here are some significant achievements that show this progress:

• Cost Avoidance: We forecasted that migrating all 19 applications manually would cost around €1.2 million. The AI-driven approach landed at approximately € 360,000.
• Accelerated Onboarding: New hires reached full productivity 40% faster, thanks to Confluence guides embedded with Copilot prompts and pre-configured DevTest Lab environments for safe experimentation.
• Widespread Process Adoption: Within three months, every engineering team leveraged Copilot beyond migrations to generate new microservice boilerplates, automate security reviews, and draft API documentation, cementing a culture of AI-assisted development.

Overcome legacy issues with JetRuby’s expert CTO Co-Pilot support and Ruby on Rails 8.0 Upgrade!

Your Legacy System Doesn’t Have to Be a Relic

The StratusNex story is a beacon for any business trapped in the past. The race to future-proof businesses is on, and the clock is ticking.

With rapid technological shifts reshaping industries, modernizing outdated systems is a survival strategy.

Here’s a closer look at the trends making now the time to act.

#1. 5G: Speed Meets Reliability

Let’s put the conspiracy theories aside — 5G is quietly revolutionizing how systems operate. Imagine seamless streaming, lag-free augmented reality (AR), or factory sensors communicating in real time.

With lightning-fast speeds and ultra-low latency, 5G is upgrading legacy systems to handle tomorrow’s data tsunami while delivering richer user experiences.

#2. AI & Machine Learning: The Automation Powerhouse

Picture this: AI tools scanning decades-old code, pinpointing inefficiencies, and suggesting fixes. Machine learning automates grunt work, optimizes systems for cloud compatibility, and speeds up migrations.

The result?

Smarter decisions, fewer human factors, and systems that evolve with your business, not against it.

#3. Low-Code: Building Faster, Smarter

Gone are the days of rewriting entire systems from scratch. Low-code platforms are like digital Lego blocks — drag, drop, and assemble apps with minimal coding. By 2025, Gartner predicts 70% of new apps will be built this way.

Why?

Because businesses can’t afford to wait years for updates. Low-code lets teams modernize piece by piece, keeping the lights on while innovating.

#4. AR/VR: Beyond Gaming Glasses

AR and VR are stepping out of the gaming world and into the boardroom.

Surgeons practicing complex procedures in virtual simulations, shoppers “trying on” clothes via AR mirrors, or remote teams collaborating in 3D workspaces.

By 2025, industries like healthcare and education will lean on these technologies to bridge physical and digital gaps.

#5. IoT: Breathing New Life into Old Systems

Legacy systems often feel like relics, but IoT is changing that. Imagine a decades-old manufacturing rig retrofitted with smart sensors, feeding real-time data to a dashboard halfway across the globe.

IoT is turning clunky infrastructure into agile, data-driven tools.

For sectors like logistics or healthcare, this means cost savings, predictive maintenance, and staying relevant.

#6. Cloud Migration: The No-Brainer Upgrade

The cloud isn’t new, but its role is growing. Think instant scalability, AI-powered analytics on tap, and ditching pricey server rooms.

By 2025, Gartner said 95% of digital projects will depend on cloud-native tools. Migrating legacy systems here is about unlocking innovations (like serverless computing) that older setups simply can’t support.

#7. Containerization: Breaking Free from Monoliths

Ever tried moving a mountain?

That’s what updating monolithic systems feels like.

Containerization tools like Docker slice these mountains into portable, manageable pieces. Many enterprises now use containers in production.

The payoff?

Faster updates, easier scaling, and systems that bend without breaking.

#8. DevOps: Teamwork Makes the Dream Work

DevOps is the glue holding modernization together. By merging development and operations teams, businesses slash deployment times and squash bugs faster.

Nearly half of companies using DevOps report getting products to market quicker — a critical factor in fast-moving industries.

Systems optimized for speed (5G), enhanced by AI, and flexibly deployed (cloud, containers) will define tomorrow’s winners. Delaying modernization is like letting competitors write the future without you. Time to pick up the pen.

JetRuby’s Expertise in Legacy Modernization

JetRuby has over 15 years of experience improving healthcare, finance, and logistics systems. Our certified team combines technical skill and strategic planning to ensure your code meets modern standards and future needs.

AI Tools Beyond GitLab Duo

JetRuby uses several AI tools in addition to GitLab Duo and GitHub Copilot:

• AWS CodeWhisperer: This tool helps update cloud infrastructure and makes managing code in the cloud easier.
• SonarQube AI finds security weaknesses in older systems built with Java, .NET, and COBOL and helps to keep them safe.
• Custom and open-source LLMs: We train these models on specific industry codes, such as healthcare standards and banking rules. They help us update code while considering each sector’s unique needs.

These tools help JetRuby improve its projects and maintain strong security standards.

Security

JetRuby focuses on security and compliance by using a zero-trust model. We do not automatically trust anyone inside or outside our network. Instead, we verify every access request.

We comply with essential regulations like GDPR and HIPAA to protect the EU’s healthcare data and privacy rights.

To keep our systems safe, we follow the OWASP Top 10 guidelines. This helps us manage common risks such as SQL injection (SQLi) and cross-site scripting (XSS).

We also use NIST frameworks for threat modeling, which helps us identify and address potential risks to critical infrastructure.

Post-Migration Support

JetRuby provides 24/7 monitoring and regular audits using AI to:

• Improve performance with predictive analytics.
• Fix security issues with automated threat detection.
• Expand systems using Kubernetes or serverless setups.

Partnerships & Methodologies

We innovate through strong partnerships and innovative processes.

Here are the key principles that guide this approach:

• We work with leading companies, such as Microsoft Azure, Docker, and GitLab, to access the latest tools.
• Our 6-week agile development cycles use Continuous Integration and Continuous Deployment (CI/CD) pipelines, AI testing, and stakeholder feedback to improve our process.

Why Choose JetRuby?

• 95% Code Coverage: We use AI-generated tests to validate every software update.
• 50% Faster Time-to-Market: Our reusable libraries and pre-set pipelines help us deliver solutions more quickly.
• We support new technologies like 5G, the Internet of Things (IoT), and quantum-ready encryption to keep our solutions secure and relevant.

At JetRuby, we resurrect systems, empower teams, and turn technical debt into tomorrow’s competitive edge. Whether your monolith is Java, .NET, or other, we’ll help you build a bridge to the future.

Start your new journey! The best time to modernize was yesterday.

The second-best? Now.