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Most companies don't decide to modernize their Java stack. They get forced into it, usually by a security scan that won't go green.
That's what Mathias, managing partner and Java developer at Optis, keeps running into. Clients aren't reaching out because they read about Java 25's new features. They're reaching out because they can't ship. High-severity vulnerabilities are blocking their release pipeline.
"The demand to modernize is coming more and more from the clients themselves," he says. "Security and code scanning is being pushed hard, and they see applications with problems."
So the question isn't really why modernize. It's why companies wait until they're forced to.
Fear of regression. You have an application that's been running reliably for years, and you're about to touch things nobody has touched in almost as long. That's a real concern.
Budget unpredictability is the other side of the coin. Small applications are easy to scope. Larger ones with lots of integrations get tricky.
When Mathias sizes a migration, two things are often crucial: the number of integrations with other systems, and how authentication and authorization are handled. "The biggest changes over the years are almost all in authentication and authorization," he says. "That's usually where most of the work sits."
Business logic, by contrast, tends to survive version jumps well. Java stays backwards compatible. Code from 2014 mostly runs fine on Java 25.
Before you think about migration strategy, you need a rollback plan. If something goes wrong in production, how quickly can you get back to the previous state? On containerized infrastructure that's straightforward: you swap images. On dedicated servers with manual installs, it's a different conversation. Either way, that question needs an answer before the first line of code changes.
The right migration strategy follows from there. Some clients want to move application by application to keep risk contained. Others prefer to get it all done at once and move on. Neither is wrong. What matters more is how well the existing codebase is understood, how critical the application is, and how much tolerance there is for disruption.
Once that's settled, the usual upgrade flow follows a sensible split: upgrade first, optimize second. Phase one is just getting to the new version: update Java, update Spring Boot, fix what breaks, don't touch business logic. Phase two, a few weeks after running in production, is going back to look at what the newer language features actually let you improve.
"Do the upgrade first, run it on the new version for a few weeks, and then go look at which code you can optimize," Mathias says. "That's what I'd do."
It's more economically efficient to keep an application up to date than to wait until it's really necessary.
Before getting to what's better, it's worth stressing about what older Java code actually costs you. Libraries that haven't been updated in years accumulate CVEs. Codebases with sprawling boilerplate are harder to read, harder to test, and harder to hand off to a new developer. And some patterns that were standard in Java 8 have subtle failure modes that only show up under real-world conditions.
Upgrading fixes the security exposure. But it also gives you language features that make the codebase genuinely more maintainable. Here are two that show up repeatedly in practice.
Records collapse Java's traditional data class boilerplate into a single line. What used to look like this:
// Java 8
public class Product {
private final String name;
private final double price;
public Product(String name, double price) {
this.name = name;
this.price = price;
}
public String getName() { return name; }
public double getPrice() { return price; }
}
Now looks like this:
// Java 25
public record Product(String name, double price) {}
Same result. No ceremony. And because Records are immutable by default, you also eliminate an entire class of bugs where something modifies an object it shouldn't. With traditional mutable classes, that kind of accidental mutation is surprisingly easy to miss in a code review.
Pattern matching removes the cast-after-check pattern that Java developers have been writing for years:
// Java 8
if (shape instanceof Circle) {
Circle c = (Circle) shape; // redundant cast
return Math.PI * c.radius() * c.radius();
}
// Java 25
if (shape instanceof Circle c) {
return Math.PI * c.radius() * c.radius();
}
Less code isn't just cleaner. The old pattern had a real flaw: if you checked for one type but accidentally cast to another, the compiler wouldn't catch it. Pattern matching closes that gap because the check and the cast are a single operation.
On the Spring side: the new REST test client makes API testing cleaner and less verbose. API versioning support means you can run v1 and v2 side by side while external consumers migrate at their own pace. And null safety annotations let your IDE flag potential null pointer issues before they reach production. "It causes issues all the time," Mathias says, "because as a developer you often just don't think about it until something breaks in production."
Most clients Mathias works with are modernizing because they had to. That's expensive.
The alternative is treating it as maintenance: small, regular upgrades instead of one big forced migration every five years. One of his clients built this into their deployment process: every release runs a code scan, and high or critical vulnerabilities block the ship. Hard policy, painful to enforce at first. But now they're never more than a step behind.
"It's more economically efficient to keep an application up to date than to wait until it's really necessary," Mathias says.
If you're sitting on an application that hasn't been touched in a few years and want to know where you actually stand, Optis can scan the codebase, map what needs to change, and give you a realistic picture before any work starts.
