Legacy Application Modernization Services

Application modernization is the work of moving an existing production system onto a currently supported platform, framework and architecture without rebuilding it from scratch. Java modernization is usually four things at once: a JDK upgrade, a move off a legacy application server, the javax to jakarta namespace change, and services pulled out of a monolith where a real domain seam exists.

Most legacy systems are not broken. They are expensive to change, hard to hire for, and stuck on frameworks that no longer receive security patches. A full rewrite sounds like the clean answer, but big-bang rewrites routinely overrun, ship late, and lose business logic that lived only in the old code. We take a different route.

We modernize legacy applications incrementally. New, well-tested services grow alongside the running monolith and gradually take over its responsibilities until the old code can be retired safely. You get continuous business value, lower risk, and a production system that never goes dark during the transition. Every engagement is led by our founder Gopal Sabhadiya (a Toptal engineer, Expert-Vetted on Upwork, ex-Infosys Specialist Programmer), backed by a focused team of 10.

Incremental modernization with the strangler fig pattern

The strangler fig pattern lets us modernize a live system without stopping it. We place a facade or API gateway in front of the existing application and route incoming requests through it. At first, every request still goes to the old monolith. As we build each replacement capability as a new Spring Boot service, we update the routing so that the new code handles that slice of traffic, while everything else continues to flow to the legacy system.

This reverse-proxy-driven incremental cutover means each step is small, observable, and reversible. If a newly extracted service misbehaves, we route that path back to the monolith in seconds. Over time the new services take over more and more, and the original codebase shrinks until it can be decommissioned. This is how we avoid the budget overruns and lost business logic that sink big-bang rewrites, and how we keep your team shipping features during the migration instead of freezing development for a year.

Zero-downtime is the point of working this way, and it takes more than a rolling deploy. Schema changes go out in expand-and-contract steps: add the new shape, write to both while old and new code are live, backfill, then drop the old shape once nothing reads it. Route changes go out behind a flag, so a slice of traffic proves the new service before the rest follows, and a rollback is a routing change rather than a redeploy. On Kubernetes we use rolling updates and readiness probes so the old pods keep serving until the new ones are genuinely ready. No maintenance page, no cutover weekend.

From Java EE to Spring Boot and Jakarta EE

The most common modernization we run is Java EE to Spring Boot. We lift applications off legacy application servers such as WebLogic and WebSphere onto self-contained Spring Boot deployments. EJBs are refactored into Spring beans with declarative transactions and Spring AOP for cross-cutting concerns; JNDI-bound datasources move to externalized configuration through environment variables, ConfigMaps, and Secrets; and the persistence layer is modernized on Hibernate and JPA.

The javax-to-jakarta namespace change is a hard requirement for anything moving to Spring Framework 6 and Spring Boot 3, which target Jakarta EE 10. We treat this as automated code transformation rather than manual editing, using OpenRewrite recipes, Eclipse Transformer for bytecode-level migration, the Apache Tomcat Jakarta migration tool, and the Spring Boot Migrator where it fits. For teams on Spring Boot 2, we run the Spring Boot 2 to 3 migration as a planned sequence: dependency upgrades, the jakarta namespace switch, configuration property changes, and then verification against your existing test suite. Every transformation is committed in small, reviewable steps.

Java 8 to 11 to 17 to 21, one hop at a time

Spring Boot 3 needs Java 17 as a minimum, so a Java EE to Spring Boot move is usually a JDK upgrade wearing a disguise. We run it as separate hops, 8 to 11, then 11 to 17, then 17 to 21, because each hop breaks something different and a red build tells you far more when only one thing changed. The 8 to 11 hop is where the JDK stopped shipping the Java EE and CORBA modules, so JAXB, JAX-WS, and the javax.annotation types vanish from the classpath and have to come back as explicit dependencies. It is also where dated bytecode tooling gives up: ASM-based libraries, cglib proxies, and older Hibernate or Lombok versions fail on the newer class-file version until they are upgraded.

The 11 to 17 hop is about encapsulation. Java 17 made strong encapsulation of JDK internals permanent and reduced the --illegal-access escape hatch to a no-op, so reflection into internals that used to print a warning now fails outright, and any library reaching into sun.misc or other JDK internals has to be updated or replaced. The 17 to 21 hop is the one teams ask for by name: virtual threads went final in Java 21 and Spring Boot 3.2 turns them on with a single property. The caveat we check before anyone gets excited is that on Java 21, blocking inside a synchronized block pins the virtual thread to its carrier thread, so hot paths that synchronize need to move to ReentrantLock before the change pays for itself. Java 25 is the newer LTS and it carries the fix that removes that pinning caveat, so we pick the landing version with you instead of defaulting to the newest one. Each hop ships as its own pull request, verified against your existing test suite and running in production before we start the next one.

Breaking the monolith into microservices

We do not split a monolith for its own sake. We decompose it along bounded contexts, the natural seams in your domain where data and behavior genuinely belong together. Authentication, billing, catalog, and reporting are typical first candidates because they have clear boundaries and clear ownership.

Our USP.org engagement shows this in practice: we extracted authentication into a dedicated Spring Boot and Spring Security microservice that is horizontally scalable and runs in production for USP.org's national-scale user base. Each extracted service is containerized and runs on Kubernetes, with independent scaling and deployment. The result is reduced technical debt, faster release cycles, and an architecture your team can reason about service by service instead of wrestling with one large, fragile codebase.

Should you refactor or rewrite a legacy application?

In most cases, refactor incrementally rather than rewrite. Not every component needs the same treatment, so we assess each one against five paths: rehost, replatform, refactor, rearchitect, and retire. Those come from the R-strategies AWS published for cloud migration, with refactor and rearchitect counted separately because they are very different amounts of work. Some components are best lifted onto a modern runtime with minimal change; others justify deeper refactoring or rearchitecting into services; a few are candidates for retirement once their functionality moves elsewhere.

We deliver an enterprise application modernization roadmap that sequences this work to maximize business continuity and minimize risk. That roadmap, component by component, is the first thing worth asking any application modernization company for, well before anyone quotes you a rewrite. The default answer to refactor versus rewrite is almost always incremental refactoring, because it preserves hard-won business logic and keeps the system earning revenue throughout. We recommend a rewrite only where the existing code is genuinely beyond salvage, and even then we phase it behind the same strangler fig facade.

Incremental modernization or a big-bang rewrite?

We almost always modernize incrementally. Here is the trade-off.