How 3 Java Champions are using GitHub Copilot for increased productivity

Three Java Champions with over a decade of Java experience discuss integrating GitHub Copilot into their workflows to enhance productivity without compromising software quality. They highlight the need for developers to provide context, validate AI-generated code, and maintain accountability for final outputs. The developers describe using Copilot in both production and open-source projects, emphasizing that speed alone does not address complex engineering challenges.

One developer used Copilot to build a web application for Hypersistence Optimizer, reducing a task previously estimated at weeks to just two days. The tool acted as a collaborative assistant, enabling rapid implementation of features while requiring human review to ensure correctness and maintainability. The developer noted that Copilot accelerates prototyping and investigations but still relies on human judgment to refine and approve AI-generated solutions.

A developer created custom skills for Copilot to align AI-generated text with their writing style, trained on 65 published articles. This approach ensures generated documentation reflects their voice and adheres to principles like expressiveness and simplicity. They also adjust model types based on task complexity, using heavier models for thorough reasoning and lighter ones for simpler tasks like generating reports or verifying documentation.

A coach working with teams emphasizes incremental improvements over large-scale transformations, focusing on solving immediate problems and defining measurable progress. They integrate practices like TDD, refactoring, and continuous delivery, while leveraging AI tools to enhance team autonomy. The coach values collaborative decision-making and sees AI as a tool to facilitate learning and problem-solving alongside team members.