Artificial Intelligence is becoming an increasingly common part of modern software development workflows. While many discussions focus on whether AI will replace developers, my experience has been much more practical: AI has helped me work faster, learn new technologies more efficiently, and simplify parts of both development and testing activities.
Over the past months, I started integrating AI tools into my daily workflow, especially when working on tasks outside my primary area of expertise. One of the most valuable examples was during the implementation of a new VS Code extension.
Building a VS Code Extension with AI Assistance
Although I did not have strong JavaScript experience, AI helped me generate code, understand implementation patterns, and structure functionalities that would normally require significantly more research and experimentation.
Instead of spending large amounts of time searching through documentation or debugging syntax-related issues, I was able to focus more on the actual functionality and the end goal of the extension. AI accelerated the learning process and made it possible to prototype and implement features much faster than expected.
What stood out the most was not just code generation itself, but the ability to quickly explore different implementation approaches and iterate faster during development.
AI Improves Speed — But Human Validation Remains Essential
One important lesson I learned is that AI-generated code should always be reviewed carefully.
There were situations where I requested a relatively small change, but the generated solution included around 30 lines of code for something that realistically required only a few lines. After reviewing the output and providing additional feedback, the AI generated a much cleaner and simpler implementation.
This highlighted an important aspect of using AI in software development: while it can accelerate delivery, developers still need to evaluate architecture decisions, readability, maintainability, and overall code quality.
AI can produce working solutions quickly, but human oversight remains critical.
Simplifying Refactoring and Code Cleanup
Another area where AI proved useful was code refactoring and cleanup.
In larger projects, identifying unused logic, redundant code, or opportunities for simplification can become time-consuming. AI helped speed up this process by identifying sections of code that could be removed, reorganized, or improved for better readability and maintainability.
While the final decisions still required manual review, AI significantly reduced the time spent analyzing and restructuring existing codebases.
Using AI in Test Automation
Beyond development tasks, I also explored AI-assisted workflows in software testing and automation.
AI helped me identify suitable tools for automated testing of a mobile application and assisted with generating the initial structure for automated tests targeting specific application pages.
This reduced the amount of time spent researching frameworks, configuring environments, and setting up repetitive testing scenarios. Instead of starting from scratch, I could focus more on refining the tests and adapting them to the application’s actual business logic.
AI as a Productivity Tool
The biggest advantage AI brings to my workflow is speed.
It accelerates repetitive tasks, helps connect multiple technologies more efficiently, assists with documentation, and lowers the barrier when working with unfamiliar technologies or frameworks.
At the same time, AI is not a replacement for technical knowledge or engineering judgment. Generated solutions still require validation, optimization, and context-aware decision-making.
When used correctly, AI becomes less of an automated replacement and more of a productivity tool that helps developers and testers work more efficiently.
Final Thoughts
My experience using AI in software development and testing has been overwhelmingly positive. It has helped reduce development time, simplify experimentation, and improve productivity across multiple areas of work.
However, the most effective results come from combining AI-generated assistance with human expertise, critical thinking, and proper code review practices.
AI can significantly accelerate software delivery — but the responsibility for building maintainable, reliable, and high-quality solutions still belongs to the engineering team.
Author: Corina Man, Developer
Corina is one of those people every team relies on. She’s committed, dependable, and always ready to jump in and help a colleague no matter the challenge. Her positive energy and sense of fun make even the busiest days feel lighter, and she brings that same spirit into everything she does. Outside of work, Corina is a proud mom to two adorable girls and a passionate reader who’s rarely without a good book. Whether she’s supporting her team or getting lost in a story, she brings care, consistency, and a genuine love for what she does.
