Modernizing a legacy Java application can seem overwhelming, especially when the codebase has been around for years and lacks a solid foundation of automated tests. However, modernization need not be a leap into the unknown. Organizations can incrementally improve legacy applications and transform them into maintainable, flexible codebases that support modern business needs by applying proven techniques and starting with testing.
This article focuses on best practices that allow tackling each stage of modernization in an organized, low-risk manner. We begin by explaining why introducing tests should be the first step before a large-scale refactoring – and then discuss how to embed tests in a codebase that was not designed for it. From there, we explore additional best practices such as characterizing existing behavior, injecting dependencies to promote modular design, and using specialized tools to speed up the process. Finally, we show how a strong testing foundation enables safe, incremental refactoring.
Testing as the First Step in Java Modernization
In legacy projects, developers often hesitate to change the code because they lack confidence that something critical will not break. This is due to the absence of automated tests. A tweak in one method could introduce regressions elsewhere, and no one would know until production defects appeared. Over time, this paralysis leads to a vicious cycle: the codebase grows ever more fragile, new features are delayed or patched in hurriedly, and technical debt keeps piling up.
Tests serve several purposes simultaneously. First, they verify current behavior. If your tests pass, you know the code still does what it did before you changed anything. Second, they become a living form of documentation. When you see test names describing what the system should do under various inputs, it clarifies the code’s purpose more concretely than an outdated comment or a rarely updated wiki. Third, a thorough test suite fosters incremental improvement. You can refactor a method, run tests within seconds, and confirm that all is well. That immediate feedback turns app modernization from a stressful activity into a routine part of software upkeep.
In older Java applications, you may face the added challenge of the code not being structured for testability. Perhaps classes construct their dependencies (e.g., new DatabaseConnector()) or carry large blocks of logic that rely on external systems like file storage or proprietary frameworks. This design means you cannot simply import JUnit, write a few lines, and be done. Introducing testing will likely require small but carefully considered changes – making sure you can instantiate classes in isolation, supply mock objects in place of real ones, or pass in constructor parameters rather than using static singletons. While this might require initial effort, it pays off. Once you have test harnesses around critical parts of the system, modernization becomes a matter of systematic refactoring rather than a leap of faith, empowering you with a sense of control over the process.
How to Introduce Testing into a Legacy Java Codebase
For many teams, the biggest challenge is adding tests to code that was never designed for them. There is a paradox: you need to refactor to make the code testable, but you want tests to ensure that refactoring doesn’t break anything. The solution is to do small, safe adjustments – sometimes called “pre-refactorings” – that allow the code to be tested, followed by an incremental process of writing tests. As tests accumulate, you can refactor with much greater confidence.
2.1 Retrofitting Unit Tests
Begin by creating unit tests for the most critical or error-prone sections of the code. This typically includes identifying areas in the code where you can inject test inputs or replace dependencies. For example, if a method calls a hard-to-use component (like a static database connector), you might first refactor that call into a method that can be overridden or use dependency injection.
Write basic assertions that capture the current behavior (even if the behavior isn’t ideal) – the goal is to document and pin down what the code does before you change it. These initial tests are your baseline for future refactoring, providing a secure starting point for the modernization process.
2.2 Characterization Tests
Use characterization tests to understand the existing behavior of the legacy code. When you cannot determine what a legacy function is supposed to do (perhaps due to missing specs or complexity), write tests that simply record the existing outputs for given inputs, effectively mapping the behavior. For example, if you have a method calculateInvoiceTotal(order) with no documentation, you can write tests with various sample order inputs and assert whatever result it currently produces.
These tests act as a safety net. If you refactor calculateInvoiceTotal and a characterization test fails, it means you changed behavior that clients might rely on. Characterization tests are beneficial for reversing entropy in legacy applications by stabilizing behavior first.
2.3 Approval Testing
Approval testing captures the system’s current behavior, especially when dealing with larger units of code. The idea is to take a snapshot of the system’s output for a given scenario and have a human “approve” it as the correct baseline. Subsequent test runs compare the current output to the approved output; any differences cause the test to fail.
This is especially useful when rewriting a module: You can wrap the old implementation in tests that record its outputs (the Golden Master), then refactor or replace it and verify that the new code produces the same outputs for all test cases.
2.4 Combination Approvals
This is an extension of approval testing, where you systematically cover combinations of inputs. Instead of writing dozens of individual tests for different input values, you let a framework generate combinations for you. For instance, if a function’s behavior depends on two integers, you might specify a set of interesting values for each and then automatically verify the output for every pair. Combination approval testing can dramatically reduce the effort in writing exhaustive tests for complex legacy logic.
2.5 Dependency Injection
Many legacy codebases instantiate dependencies directly (e.g., calling new or using singletons within methods). Dependency injection (DI) means giving an object its needed collaborators from the outside, rather than creating them internally. This can be done through constructors, setters, or interface injection. For example, if a ReportService class uses a Database connection, the legacy code might call db = new Database() inside the service. To inject this dependency, you would modify ReportService to accept a Database object via its constructor or a setter. Now, you can supply a fake or in-memory Database implementation in tests. DI makes code more testable by removing hardwired dependencies, allowing you to substitute test doubles. It creates natural seams for unit testing. (We’ll see a code example in the next section.)
2.6 Shallow and Deep Testing
When writing tests for a complex method, it’s often helpful to start with the shallowest branch – the most straightforward case or the early exit path – and then work deeper. Testing the shallow branch (for example, when a method returns immediately due to an input check) is more straightforward and gives quick feedback that your test harness is working.
Once the basics are covered, you can write tests for deeper, more complex branches of logic, one by one. This incremental approach prevents you from getting overwhelmed and ensures that even if you can’t test everything at once, you have partial coverage that steadily improves. Over time, your tests will reach those deep corner cases as you gain more understanding of the code.
Using these strategies, you can gradually wrap legacy Java code with a protective suite of tests. It’s often necessary to do some gentle refactoring (such as injecting a dependency) before you can write a test – just make sure to keep those changes minimal and safe, and rely on manual testing or code review until the first tests are in place.
Once you have tests covering the current behavior, you’ve effectively mapped the minefield. You can proceed to refactor with much greater confidence.
Tools That Help Modernize Java Applications
Building tests and refactoring older Java code is easier when you leverage specialized tooling. The right tools can automate much of the work, such as generating initial test classes, identifying code smells, or analyzing coverage. They also provide quick feedback on changes so teams can iterate confidently.
Modern IDEs (IntelliJ IDEA, Eclipse, etc.) provide automated refactoring features that are extremely valuable for legacy code. Operations like “Extract Method,” “Inline Method,” “Rename,” and “Change Signature” can be done with a click, ensuring that all references are updated safely. Use these tools to avoid human error, especially in a big codebase. For example, if you have a 500-line method, highlight a coherent chunk and use the Extract Method to give it a name – the IDE will handle moving the code and updating local variables. Automated refactorings are usually behavior-preserving by design, but running tests after each change is still wise.
AI-driven tools such as Diffblue Cover can automatically generate test cases for an especially large or tricky codebase. This approach can rapidly bootstrap coverage in sections of the code that otherwise would require extensive manual test writing. Developers then refine or augment these auto-generated tests with domain-specific checks, ensuring that corner cases and nuanced business rules are validated. By combining specialized tooling with incremental strategies like characterization testing, you build momentum and reduce the time spent wrestling with archaic designs.
Transitioning from Testing to Refactoring
You can begin the refactoring phase once you have a reasonable amount of test coverage. Refactoring is about improving the internal structure of the code, breaking up gigantic methods, extracting cohesive classes, or shifting to a microservices approach without changing the system’s external behavior. The presence of tests makes this possible: Whenever you rename a variable, reorder logic, or extract a new class, you immediately run the tests. If they pass, you have strong evidence that you have not broken anything. If they fail, you can see which test blew up and fix the problem before it goes live.
A recommended pattern is to refactor in small steps. For instance, you might extract a 50-line block of code from a 500-line method into its own method, run tests, and confirm success. Then, maybe you rename that new method to describe its purpose more clearly, run tests again, and proceed to the next small slice of improvement. These small steps let you isolate mistakes quickly. If something goes wrong, you revert or fix it immediately, minimizing the risk of introducing new issues. Over time, small steps add up: the code becomes more modular, the method lengths shrink, and tangles of logic are untangled. Eventually, you reach a point where the application architecture is flexible enough to incorporate further modernization goals, like migrating to microservices or adopting newer Java language features.
Another key principle is to address code smells systematically. These are symptoms of deeper problems in the code. Common smells in legacy Java apps include long methods, large classes, duplicated code, inconsistent naming, tight coupling, etc.
For example, a 1000-line class likely has multiple responsibilities; a “switch” statement that switches on an object type might hint at missing polymorphism. Whenever you detect a smell, use targeted refactorings to resolve it. For instance, extract classes to break up a god class, or apply the Strategy pattern to eliminate that giant switch. Each addressed smell reduces technical debt and improves maintainability.
A last important principle is to avoid big-bang rewrites with Java application modernization projects. While it can be tempting to discard the old system and build something fresh, many modernization initiatives fail under that approach because they take too long to show value, often replicate old bugs, or miss essential edge cases. Incremental refactoring, guided by tests, provides tangible progress each sprint. The code is always in a working state, so you are never stuck with a half-finished rewrite that cannot go to production.
Conclusion
Modernizing a legacy Java codebase can be a game-changer for an organization suffering from high maintenance costs, slow feature rollouts, and security risks. The key is to start with testing so you can refactor confidently. Introducing tests in a legacy Java application may require small structural adjustments – like injecting dependencies instead of creating them, or capturing existing outputs as “characterization” baselines – but those steps pay off by giving you a safe environment to refine the design.
Once tests are in place, incremental refactoring becomes more manageable. You can clean up small sections of the code, confirm everything still works, and move forward piece by piece. Over time, tangled monoliths yield more straightforward, more modular designs that can adopt microservices, integrate with cloud-native ecosystems, or easily handle new business requirements. The developer experience and the user experience improve alongside system reliability, accelerating feature development and reducing the risk of outages. Tools – from automated test generation utilities to mutation testers – can bring velocity to these best practices, speeding up coverage gains and spotting potential flaws.
Above all, modernization should be seen as an ongoing discipline rather than a one-time event. After your initial push to introduce tests and refactor code, keep refining. As the team becomes accustomed to running tests for every commit and regularly refactoring newly added features, the system no longer drifts back into “legacy” status. The codebase remains adaptable, so your company can respond to evolving demands in technology and user expectations. By making testing the foundation and then systematically applying proven refactoring techniques, you can transform “untouchable” Java code into a robust, future-friendly asset for your organization.
Our next article goes into detail on specific Java application refactoring techniques, demonstrating how incremental changes aligned with a strong test suite can produce a modernized, maintainable codebase.
