When I first started building my own server for a simple To-Do list app, I quickly realized just how time-consuming API testing can be. Writing APIs isn’t only about getting the functionality right, it also involves thinking through edge cases, maintaining test coverage, and updating tests every time you make a change. As someone that is a beginner to backend development, achieving high test coverage felt overwhelming.
However, thorough testing is essential for building reliable applications. That’s why discovering Keploy, an AI-powered API testing tool introduced to me through their Fellowship program, felt like a game-changer.
Some of the biggest challenges I faced were forgetting to test edge cases like invalid inputs or missing data, slow feedback loops when debugging, and tedious setup processes for integration testing.
With Keploy, I was able to automatically generate hundreds of test cases tailored to my code within minutes. These test cases captured error responses and edge scenarios. I was also able to easily integrate these tests into my GitHub Actions workflow with just a few lines of configuration.
I believe AI-driven testing tools like Keploy are valuable for everyone, from beginners to professionals in the industry. They let us spend less time on repetitive test writing and more time focusing on building features. By catching bugs earlier and covering more edge cases, AI-powered tools elevate the quality and reliability of any project.
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