Chaos meets code: Fractals as a playground for modern Java features

From mathematical rules to legacy code

Fractal engines that generate Mandelbrot or Julia sets usually start from a small mathematical formula applied repeatedly. In many legacy Java implementations, this logic appears as imperative code with nested loops, mutable fields, and classes that mix computation with rendering responsibilities.

This approach works and produces correct results. Over time, however, it often leads to verbose code and closely coupled classes. State spreads across multiple modules, responsibilities are unclear, and introducing parallelism or new features becomes difficult. The code still works, but understanding the broader impact of changes requires increasing effort.

A rendered Julia set, shown below, illustrates this contrast well. The image may look complex, but it is generated by applying a single formula repeatedly. The math itself is simple, the real challenge is expressing and structuring that logic clearly in code.

Applying modern Java Features

Modern Java offers several features that make code more readable and maintainable without altering its behavior.

Records allow core concepts like complex numbers, coordinate planes, or rendering parameters to be expressed as immutable data holders. This reduces boilerplate, clarifies which objects represent state versus behavior, and decreases the risk of subtle concurrency issues.

Virtual threads simplify parallel rendering by enabling many lightweight tasks to run simultaneously without the overhead of traditional threads. Fractal generation is ideal for this approach: each pixel row can be computed independently, scaling easily across multiple CPU cores.

Pattern matching and modern switch expressions reduce verbose type checks and casting. When combined with sealed interfaces, the compiler can verify all valid implementations of an abstraction, making control flow clearer and extensions safer.

In practice, these features let developers focus on the logic itself rather than the scaffolding, improving readability, safety, and flexibility.

From Chaotic Code to Predictable Systems

Legacy Java code is often unstructured: small changes can have unexpected effects, and closely coupled classes make the overall flow difficult to follow. Incremental refactoring helps bring predictability to the system.

Precomputing repeated values, separating computation from traversal, and grouping parameters into immutable configuration objects each make the code easier to reason about and more reliable. Streams and virtual threads enable safe parallel execution, while extracting focused methods improves readability and maintainability.

Just as fractals reveal intricate patterns through repeated application of simple rules, these small, deliberate changes transform complex legacy code into predictable, robust systems, making experimentation and future evolution safer.

Fractals as a Software Analogy

The connection between fractals and software extends beyond visual complexity. In fractals, small changes to parameters can produce dramatically different outcomes, even though the underlying rules remain simple. Similarly, thoughtful changes in code can turn a chaotic legacy system into one that is structured and reliable.

The Julia set image included in this article illustrates this point: small variations in parameters create very different patterns, yet each follows the same core formula. Modern Java features like records, virtual threads, sealed interfaces, and pattern matching, give developers the tools to achieve similar clarity and control in code while supporting experimentation and future extensions.

The Developer's Perspective

By applying modern Java practices, developers can achieve clarity, safety, and efficiency while maintaining the flexibility needed for experimentation and long-term evolution. Fractals provide a useful metaphor: complexity does not have to make a system hard to maintain, and consistent, well-structured rules can produce predictable, scalable results. Just as fractals emerge from repeated application of simple formulas, robust and maintainable software emerges from incremental, thoughtful improvements.