Do you know how much productivity you can gain using Generative AI as your assistant coder while coding?
As a senior Java developer with over two decades in enterprise software development, I’ve seen technology trends come and go. But when I first used Generative AI—particularly tools like GitHub Copilot and ChatGPT—I knew we were standing at a pivotal moment in software engineering where coding productivity is largely challenged by the presence of our AI coding partners.
Initially, I was skeptical. Could an AI assistant really understand the nuances of enterprise codebases, RESTful APIs, or test coverage? Fast forward to today, and I can’t imagine writing a microservice without AI by my side.
In this article, I’ll share how I’ve personally used AI in software development, the tools that made a difference, and how it benefited business—thanks to AI-powered coding.
My first real experience with Generative AI was using ChatGPT for some random query. It was 2 years back I was literally having no idea what prompting is. Gradually out of curiosity, I started exploring more and more. Then to check the technical capability of ChatGPT, I asked it to refactor a legacy Spring Boot application. What blew me away was how it not only cleaned up my controller but suggested improvements based on design patterns.
I got more interested and then I tried GitHub Copilot. It was like having a junior developer on my team—one that never sleeps and knows a hundred languages. I tried the VS Code plugin. As I typed, it suggested DTOs, validation logic, even mocked data for test cases. Let me give one example. I wanted to write code for constructor injection of 8 variables. As I added one parameter in the constructor, the entire constructor was suggested by copilot. I was left stunned. Just pressing a single tab, wrote the entire section for me which could have taken at least 3-4 min for me.
Suddenly, tasks that took me 3 hours were now taking just 40 minutes. And the code? Clean, efficient, and surprisingly bug-free.
I worked on a microservice recently. Normally, creating the controller, service, repository, and exception handling would take about a day. With Copilot and ChatGPT, I had the core service ready—tested and documented—in just over two hours.
The AI handled:
All I had to do was tweak and fine-tune.
I was working on an assignment where I had to understand and migrate a code written in some language I have no clue about. But my AI assistant was smart enough. I gave it the files and copilot explained the entire business functionality in seconds. Even as my AI assistant is integrated to my IDE, I just dragged a data weave file on it and asked it to write the equivalent code. Within 30 seconds, the entire file was generated and to my surprise, it was 95% accurate.
But yes, you cannot expect it to check an entire workspace and explain in one shot. At least as of now I could not see that level of maturity. We have to remember that it is our assistant and we have to give it the correct code to explain where human intelligence will come to play.
It is not always possible to remember every syntax but your Gen AI assistant can write it for you. At one point of time, I was struggling fixing an issue. I remember earlier days when I had to go to search engine and look for similar solution available over the internet. I may find a similar one or may not and even if I find, I have to rewrite it. But in presence of your coding AI assistant, you can give the code snippet and the exception details and it suggests multiple solutions. It’s like having a second pair of eyes—without burning out your team.
Thanks to AI-generated logic templates and test scaffolding, I am able to complete my development work in 50% less time than what it used to be. Higher coding productivity generates higher revenue.
When coming to the question of unit test case generation, I have used copilot to generate JUnit test cases. It’s a real magic. I select a class and give it to copilot plugin to generate test cases, and here you go. The test case is ready with all mocking. In 80% of the cases, it passes and remaining 20%, I had to fix manually or may have to re-prompt to get it corrected by my AI assistant.
The saved hours allowed us to add new features and faster time-to-market gave the client a competitive edge.
With AI helping write better unit and integration tests, bug rates dropped significantly. The AI developed tight guardrails limit the probability of bugs missed in testing phases.
While I’m thrilled about the coding productivity gains, it’s important to stay grounded. AI won’t design your domain model or help you understand complex stakeholder needs. It doesn’t know your company’s unique tech debt or user base. It’s an assistant—not an architect. Even though it is a good programmer, complex twist in requirement makes it fail. But as we are using our AI assistants, it is learning and will grow better with time.
If you’re a developer, try building your next feature with an AI assistant. If you’re a business leader, invest in training your teams on AI tooling.
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