What I Learned Building with GitHub Copilot as a Java Developer

As a seasoned Java developer, I was skeptical about AI coding assistants at first. But after trying GitHub Copilot, my view changed a lot.

I wanted to see how this tool could improve my coding. In this article, I'll talk about my move from traditional coding to using AI. I'll share the good and the bad I found.

Using AI in coding has really changed the game for me. I'll share what I learned and offer tips on using this new tool.

My Java Development Life Before AI Assistance

Before using AI tools, my Java work was tough. I spent a lot of time on simple tasks. This included writing the same code over and over, solving complex problems, and keeping my code clean.

Traditional Coding Challenges I Faced Daily

Every day, I faced:

• Writing the same code for simple classes and data transfer objects
• Creating and using complex algorithms and designs
• Keeping my code up to standard and error-free
• Fixing bugs in existing projects

These tasks took a lot of time and often had mistakes. An AI coding assistant seemed like a solution.

What Prompted Me to Try GitHub Copilot

I saw that most of my time was spent on tasks that could be automated. Trying GitHub Copilot was a way to solve these problems and work better. The idea of having an intelligent coding companion was very appealing.

As shown in the image below, AI tools like GitHub Copilot are changing how we code.

With GitHub Copilot, I wanted to make my Java work easier. I hoped to spend less time on simple tasks and more on creative coding.

Becoming a GitHub Copilot Java Developer: My First Steps

I started my journey to become a GitHub Copilot Java developer with excitement. I wanted to learn how this AI tool could improve my Java coding, focusing on Spring Boot projects.

Initial Setup and Configuration for Java Projects

My first step was to set up GitHub Copilot in my IDE, IntelliJ IDEA. The installation was easy. I installed the extension, restarted my IDE, and logged in with GitHub. After that, I was all set to use Copilot.

Setting up Copilot for my Java projects was simple. I made sure my project had the right dependencies, like Spring Boot. Copilot quickly understood my project and started offering suggestions. It fit perfectly into my coding routine.

The First Week: Surprises and Revelations

In the first week, I was amazed by Copilot's accuracy. It gave suggestions that were not just right but also made sense in the context of my code.

Some key things I learned were:

• Copilot could create basic code like getters and setters, saving me time.
• It suggested the best imports and dependencies for Spring Boot projects.
• The code it generated was often better than what I could write myself.

Using Copilot more, I realized it was more than just a time-saver. It taught me new coding patterns and best practices. It was a huge help in my growth as a Java developer, thanks to AI-assisted coding.

Navigating the Learning Curve of AI-Assisted Coding

My journey with GitHub Copilot was more than just learning a new tool. It changed how I think about coding. At first, using this AI-assisted coding tool was tough. It made me question my old coding ways.

Changing My Development Mindset

Adjusting to Copilot was a big challenge. I had to learn to trust the AI's suggestions but still control my code. It took time to figure out when to use the AI's help and when to do it myself.

Finding the Right Balance of Control

Finding the right balance was key. I had to remember that Copilot was there to help, not do my job. It was important to keep my judgment as a developer in the lead.

When to Accept and When to Reject Suggestions

Knowing when to accept or reject Copilot's suggestions was crucial. I learned this by understanding my projects and tasks. For simple tasks, Copilot was great. But for complex or critical parts, I was more careful.

Using CopilotHub was also helpful. It gave me tips and insights on using Copilot better. This improved my coding workflow a lot.

Practical Java Examples: Where Copilot Shines Brightest

My journey with GitHub Copilot has shown its power in everyday Java tasks. It excels in making boilerplate code, solving complex algorithms, and writing unit tests.

Generating POJO Classes and DTOs

Making Plain Old Java Objects (POJOs) and Data Transfer Objects (DTOs) can be slow. But with GitHub Copilot, it's quick. For example, just type // Create a User POJO class with id, name, and email and it does the rest.

• Reduced boilerplate code generation time
• Improved consistency across the codebase
• Ability to focus on more complex tasks

Implementing Complex Algorithms and Design Patterns

GitHub Copilot has helped me with complex algorithms and design patterns. For the Factory pattern, I just typed // Implement the Factory pattern for creating database connections. It gave me a solid code structure.

Unit Test Generation That Actually Works

Writing unit tests is key but can be dull. GitHub Copilot makes it easier. Just give it context, like // Write unit tests for the UserService class, and it creates detailed tests.

While Copilot has been a big help, tools like Cursor and Codeium add more value. They make my work even smoother with extra features.

Supercharging My Spring Boot Development

GitHub Copilot has changed how I work on Spring Boot projects. It's an AI tool that makes my java programming tasks easier. I've seen big improvements in my projects.

Controller and Service Layer Acceleration

Using GitHub Copilot for backend development has sped up making controllers and services. It knows my project and writes code for me. This saves a lot of time, like when I make RESTful APIs.

It also keeps my projects consistent. The service layer gets help too, with code that follows best practices.

Repository Code and Database Integration

GitHub Copilot makes working with databases easier. It writes repository code that fits my project needs. This is true for Spring Data JPA and other database tools.

Working with databases is now faster. Copilot gives me the right code for database setup and data access.

Configuration and Security Implementation

Security and setup are key for Spring Boot apps. GitHub Copilot helps a lot with these. It suggests secure ways to handle passwords and adds security tags.

It also helps with setting up different environments. This makes my java programming better and more flexible.

In short, GitHub Copilot has been a big help for my Spring Boot work. It makes many parts of my job easier, from writing code to setting up security. I'm sure it will keep being a key tool for me as I tackle more backend development projects.

Backend Development Transformed: Real Projects, Real Results

Reflecting on my journey with GitHub Copilot, I see how it's changed backend development for me. It's made API development faster and improved performance. It also helps with creating documentation.

API Development from Specification to Implementation

GitHub Copilot has been a game-changer for API development. It takes detailed specs and turns them into code, including endpoints and data models. This cuts down the time needed for setup, letting me dive into more complex tasks.

For example, in a Spring Boot project, Copilot helped create API endpoints from an OpenAPI spec. The result was a working API with little manual work. This shows how AI tools for developers can make development faster.

Performance Optimization Insights

GitHub Copilot also offers insights for improving performance. It looks at code and suggests ways to make it better. This has led to measurable performance gains in my apps, making them run smoother.

One time, Copilot suggested a better algorithm for data processing. It changed the time complexity from O(n^2) to O(n log n). This boost in performance shows Copilot's ability to offer practical optimization suggestions.

Documentation Generation That Saved Hours

GitHub Copilot has also transformed documentation generation. It creates high-quality docs from code annotations and structure. This has saved me hours that would have been spent on writing documentation by hand.

This feature is a big help in large projects with many contributors. It ensures everyone has up-to-date and accurate documentation. This improves teamwork and cuts down on misunderstandings.

Mistakes I Made and Lessons Learned

Looking back, I've learned a lot from GitHub Copilot. It has really helped me work faster as a Java developer. But, I've also had to fix some mistakes I made.

The Dangers of Code Without Understanding

One big mistake was using Copilot's code without knowing what it did. I once used it for a complex algorithm, but it didn't fit my needs. Now, I make sure to thoroughly review and test the code.

• Verify the logic and intent behind the generated code
• Test the code with various inputs and edge cases
• Refactor if necessary to improve readability and performance

Security Considerations When Using AI-Generated Code

Learning about security with AI code was important. I started using tools to scan for vulnerabilities in Copilot's code. For example, a tool caught a SQL injection risk in my code.

1. Integrate security scanning tools into your development pipeline
2. Regularly review code for potential security risks
3. Use Copilot's suggestions as a starting point, not a final solution

How I Learned to Provide Better Context

Improving how I give context to Copilot has changed a lot. By including relevant comments and specifying requirements, the code quality has gone up. For instance, telling Copilot to make code thread-safe helped a lot.

By learning from these mistakes, I've made the most of GitHub Copilot. The trick is to use AI help wisely while still understanding the code.

Beyond Copilot: My AI Development Ecosystem

I've explored more than just GitHub Copilot in AI-assisted development. Copilot is a key tool for me, but using other resources has boosted my productivity. This is true for both Java development and Spring Boot projects.

CopilotHub: The Resource That Changed My Prompt Game

CopilotHub has been a game-changer for me. It's a treasure trove of prompts and examples. These have helped me write better prompts for Copilot, leading to more precise code suggestions.

What makes CopilotHub particularly valuable:

• Curated prompts organized by category (coding, debugging, testing, etc.)
• Community-contributed resources that are constantly updated
• Searchable directory that saves hours of searching across Reddit, GitHub, and forums
• Completely free and open source, making it accessible to all developers

I've bookmarked CopilotHub and check it regularly when I need inspiration for better prompts or want to discover new ways to leverage GitHub Copilot in my Java projects.

Cursor and Codeium: Complementary Tools in My Workflow

I've also added Cursor and Codeium to my toolkit. Cursor helps me navigate big codebases, while Codeium offers more AI code completion. Together, they enhance Copilot's suggestions.

Using all three tools together makes my workflow more efficient. For instance, Copilot helps with the initial code, Cursor with navigation, and Codeium with specific code snippets in Java projects.

Creating a Personalized AI-Assisted Development Environment

I've worked on making my development environment more personalized. This means setting up my IDE with the right extensions and custom prompts. I've also fine-tuned my workflow to get the most out of each tool.

This approach has streamlined my development process. It saves me time on repetitive tasks, letting me focus on the creative parts of Java and Spring Boot projects.

How AI Complements Rather Than Replaces Java Skills

My journey with GitHub Copilot has taught me that AI-assisted coding is about making things better, not replacing them. I've seen that AI can do repetitive and complex tasks. But, there are things only humans can do.

The Human Elements AI Can't Replace

AI tools like GitHub Copilot can write code and automate tasks. But, they can't replace the critical thinking and problem-solving skills of a seasoned Java developer. For example, understanding business needs and turning them into technical solutions needs a human touch.

• Creativity in solving complex problems
• Understanding of the project's context and requirements
• Ability to make judgment calls on code quality and maintainability

How My Role Evolved Instead of Disappearing

AI-assisted coding tools have changed my role as a Java developer, not made it disappear. Now, I focus on high-level design decisions and architecture. The AI handles the routine coding tasks.

Measuring the Real Productivity Improvements

AI-assisted coding has greatly improved my productivity. By automating routine tasks, I can tackle more complex issues. This has led to measurable productivity gains. Some key improvements I've seen are:

1. A reduction in time spent on boilerplate code
2. Improved code quality through AI-assisted suggestions
3. Faster debugging and issue resolution

Overall, using AI tools like GitHub Copilot has not only boosted my productivity. It has also let me focus on the parts of Java development that need human touch.

Conclusion: Embracing the Future of Java Development

My journey with GitHub Copilot has changed how I code in Java and for the backend. Using AI tools like Copilot, Cursor, and Codeium has boosted my work speed and code quality.

I've learned to use these tools well in my work. This has helped me avoid mistakes and set up a custom AI-assisted work space. It's clear that AI tools help, not replace, human skills.

I'm looking forward to seeing how AI coding will improve my Java skills. With CopilotHub, I'm sure the future of Java coding will be bright. It will be thanks to the teamwork between humans and AI tools.