1. Updating Outdated Libraries

One of the most common issues in legacy code is the use of outdated libraries. LLMs can analyze the dependencies in a project and suggest modern replacements.

Example: Replacing Apache Commons Collections with Java Streams

Consider a legacy codebase that uses Apache Commons Collections for list filtering:

List filteredList = new ArrayList<>();
for (String item : originalList) {
    if (item.startsWith("A")) {
        filteredList.add(item);
    }
}

An LLM can suggest refactoring this code using Java Streams, which were introduced in Java 8:

List filteredList = originalList.stream()
                                .filter(item -> item.startsWith("A"))
                                .collect(Collectors.toList());

This not only simplifies the code but also leverages modern Java features, improving performance and readability.

2. Automating Code Refactoring

LLMs excel at identifying code smells and suggesting automated refactoring, reducing technical debt.

Example: Replacing Nested Loops with Stream Operations

Legacy code often contains nested loops that are both difficult to read and inefficient:

List largeOrders = new ArrayList<>();
for (Customer customer : customers) {
    for (Order order : customer.getOrders()) {
        if (order.getTotal() > 1000) {
            largeOrders.add(order);
        }
    }
}

An LLM can automatically suggest a more modern approach using flatMap with Streams:

List largeOrders = customers.stream()
                            .flatMap(customer -> customer.getOrders().stream())
                            .filter(order -> order.getTotal() > 1000)
                            .collect(Collectors.toList());

This refactoring not only improves performance but also enhances code readability.

3. Improving Performance

LLMs can also suggest optimizations by analyzing code for inefficiencies, recommending more performant algorithms or data structures.

Example: Optimizing a Sorting Algorithm

A legacy system might use a custom sorting algorithm that is less efficient than Java’s built-in methods:

public void sortNumbers(int[] numbers) {
    // Bubble sort implementation
    for (int i = 0; i < numbers.length; i++) {
        for (int j = 0; j < numbers.length - 1; j++) { if (numbers[j] > numbers[j + 1]) {
                int temp = numbers[j];
                numbers[j] = numbers[j + 1];
                numbers[j + 1] = temp;
            }
        }
    }
}

An LLM can suggest replacing this with the more efficient Arrays.sort method:

public void sortNumbers(int[] numbers) {
    Arrays.sort(numbers);
}

This change can significantly improve the performance of sorting operations, especially for large datasets.

Conclusion

Modernizing legacy Java code is a challenging but necessary task for many organizations. LLMs provide a powerful toolset to assist in this process, offering intelligent code suggestions and plenty of refactoring opportunities. By leveraging LLMs, developers can reduce technical debt, improve code quality, and ensure that legacy systems remain maintainable and scalable in the long term.

The examples provided illustrate just a few of the ways LLMs can be employed in legacy code modernization. As these models continue to evolve, their potential to transform software maintenance and modernization will only grow, making them an indispensable resource for Java developers navigating the complexities of legacy systems.