Leave the tedious work to AI. Our AI-powered refactoring tool, CodeScene ACE (still in beta), makes it easier than ever to automatically refactor complex code and remediate technical debt. With one click, ACE suggests a cleaner, more efficient version while maintaining the code’s behavior.
It means much more time to develop fun features and interesting capabilities, instead of wading through overly complicated code. Refactoring complicated legacy code has never been this easy — or this fun! 😎
AI-Refactor Complex Java Code
In our demo, you’ll see a piece of Java code with numerous branches and complex expressions. CodeScene ACE identifies these complexities, demonstrates that the method is indeed complex, explains how we identify complex methods, and details the metrics we use. This type of code can be a nightmare to maintain.
But that's not all. With a single click, you can automatically refactor the code. ACE suggests a cleaner, more efficient way to write it. This isn’t just about simplifying the code; it’s about ensuring that the refactoring improves the code’s overall health while preserving its original behavior.
Fact-Validation Layer on Top of the AI
One of the unique aspects of CodeScene ACE is its built-in validation. Before suggesting changes, ACE checks to ensure that the refactoring truly enhances the code without breaking anything. This gives you confidence that applying the refactor won’t introduce new issues into your software.
After applying the refactoring, you’re left with cleaner, more maintainable code, making it easier to reason about and improve further. Refactoring legacy code doesn’t have to be a daunting task anymore. With tools like CodeScene ACE, it becomes a more manageable and even enjoyable process for developers.
Why Focus on Improving Existing Code?
We've previously written about guardrails, covering in-depth why you need to use them for AI-assisted coding and why code quality is even more important now in the AI age, as software development teams worldwide adopt AI coding tools.
A 2023 study found that popular AI assistants generate correct code in only 31.1% to 65.2% of cases. Similarly, in our Refactoring vs. Refuctoring study, we found that AI breaks our code in two out of three refactoring attempts(!). A human developer shipping code of such low quality would have a hard time keeping their job, so why do we accept such a low performance rate when it comes to AI?
An easy answer is that AI serves as a shortcut: a developer might know what code they want, and the AI might help them get there faster. A skilled developer can then inspect, tweak, and correct the resulting code, hopefully in less time than if they’d started from scratch.
However, when working with code, the actual typing isn’t the hard part. The bottleneck is the effort required to understand existing code. As AI accelerates the pace of coding, human readers will have a hard time keeping up. The more code we have, the more difficult it becomes to understand a software system. Acceleration isn’t useful if it drives our projects straight into a brick wall of technical debt.
