DevOps leaders are under pressure to modernize Java applications faster than ever, driven by cloud migration, the need for continuous innovation, security demands, and AI. But many modernization initiatives are slowing down or failing to scale, not because of tooling, talent, or even architecture, but because of technical debt.
The Drag on DevOps
Technical debt accumulates over years of iterative development in the form of unused features, outdated dependencies, legacy test code, and abandoned feature flags that build up like past strata in the earth’s crust. In large Java estates, this debt becomes deeply embedded in the codebase, an enormous weight of unnecessary complexity that acts as a hidden drag on productivity. Enterprises are often shocked to discover that as much as 50% of their codebase may be unused.
Most modernization efforts don’t remove this debt. They just carry it forward. For DevOps teams, that creates real friction:
- Slower CI/CD cycles due to bloated codebases
- Increased risk during upgrades and deployments
- More time spent maintaining code that delivers no value
The result is a paradox: modernization initiatives intended to accelerate delivery actually end up slowing it down.
The End-of-Support Cliff Is Approaching
This challenge is about to intensify.
Java’s Long-Term Support (LTS) versions – 8, 11, 17, and 21 – are all approaching end-of-support within a narrow window between 2029 and 2032. For DevOps leaders, this means:
- Coordinating multiple upgrades across distributed application portfolios
- Maintaining security compliance as patch availability disappears
- Managing increased deployment risk under compressed timelines
Without reducing technical debt first, these upgrades become significantly more complex and risky.
Tech Debt Threatens Your Business
On August 1, 2012, Knight Capital Group, an American global financial services firm, lost roughly $440 million in 30 minutes due to a software glitch caused by technical debt.
During a manual deployment of their new market-making software called Retail Liquidity Program, engineers failed to update one of eight servers. That server repurposed old, deprecated code known as “Power Peg” that was designed to execute a test suite that would buy high and sell low.
In just 30 minutes, Knight Capital Group accumulated huge losses that almost brought down one of the largest market makers on the New York Stock Exchange.
Why Existing Approaches Fall Short
Most teams attempt to manage technical debt using static analysis, logging, or AI-enhanced tools. But these approaches rely on inference rather than evidence.
- Static analysis assumes code is active if it’s reachable
- Logs provide incomplete signals
- AI models lack direct visibility into runtime behavior
Without observing what actually runs in production, DevOps teams are forced to err on the side of caution, which means dead and unused code never gets removed.
A Better Model: Production-Driven Insight
To break the cycle, DevOps needs a more reliable signal: runtime truth.
Azul Code Inventory, a feature of Azul Intelligence Cloud, automatically collects information from the Java Virtual Machine (JVM) as it runs code in production. It does so for any JVM without imposing operational overhead, then presents this information in a report so you can instantly identify which libraries, classes, and methods of a Java application are present in the codebase but never used. By monitoring the JVM in production, it’s possible to see exactly which code paths are executed, and which are not.
This approach transforms technical debt from a guessing game into a measurable, actionable problem:
- Automatically identify unused libraries, classes, and methods with precision.
- Prioritize cleanup based on real usage.
- Reduce risk by focusing only on code proven to be inactive.
For DevOps teams, this is a shift toward data-driven modernization.
Safe Automation at Scale
Of course, identifying dead and unused code is only half the challenge. Removing it safely without breaking builds or deployments is critical.
OpenRewrite is an open-source auto-refactoring engine whose recipes can be used as tools that work through large-scale code changes and execute them safely across multiple repositories.
By combining Azul Code Inventory’s deep runtime visibility and Java expertise with OpenRewrite’s powerful engine for automated, multi-repository, rules-based code refactoring, enterprises can automatically identify, flag, and subsequently remove dead and unused code safely and securely.
Turning Technical Debt into Capacity
The payoff is huge. When DevOps teams eliminate unused code:
- Pipelines run faster.
- Security exposure decreases.
- Deployment confidence increases.
- Engineering capacity is freed for higher-value work.
Even modest reductions in maintenance overhead can translate into meaningful gains, often equivalent to adding multiple engineers back to the team.
The Bottom Line
When Java modernization fails, it is more often because of accumulated complexity than any other reason. For DevOps leaders, the best way to deal with this accumulated complexity is clear:
- Make technical debt visible through runtime insight.
- Automate its safe removal.
- Align modernization with real production behavior.
Those who do so will be better able to navigate the upcoming end-of-support wave without difficulty. And in doing so, they’ll finally deliver on the promise of DevOps: faster, safer, more reliable software delivery.
