ChatGPT is a powerful AI bot that can hold a conversation, clarify difficult concepts, and amaze you with its resourcefulness and ability to answer a wide range of questions. OpenAI made ChatGPT, a model for processing natural language that has been used a lot on the internet since it came out. Within five days of its release, this chat-based tool has attracted over 1 million users. Without a doubt, it's by far the biggest step forward in the field of "generative AI," so the hype is well-deserved.
ChatGPT's ability to simulate real-world user interactions opens up a lot of business possibilities in many different areas. API testers also share a pool of mixed opinions on ChatGPT. Some are rooting for ChatGPT as a powerful helping hand, while others say it can’t figure things out on its own.
API testing is a complicated process that requires making a lot of test scripts to simulate how different users would use the API. From a tester’s perspective, this is a time-consuming and error-prone process, especially when it comes to working with complex APIs with different parameters. The job of a tester is to write test cases that mimic each real user interaction flow. This is very time-consuming and almost impossible to do manually. So the end goal of a tester narrows down to achieving some kind of automation in writing such test cases.
The intention of a tester to use ChatGPT is very clear:
Reducing the manual effort that goes into writing test scripts
To accomplish things in a timely manner
Since ChatGPT uses NLP, testers just need to provide test descriptions in a human-readable form, and the chatbot can provide test cases based on that description. So, if testers use ChatGPT, they can better test how well API systems work and how reliable they are. This can make testing go faster, save money, and give more accurate results than traditional methods.
As much as ChatGPT seems like a dream come true for the future of API testing, we should not forget that it is still in its infancy and thus can’t be relied upon completely.
OpenAI clearly states on its platform that ChatGPT can generate false information occasionally. Aligning with that, StackOverflow has also banned ChatGPT answers to programming questions.
Overall, ChatGPT is a great tool of the future that, as of now, comes with warnings. OpenAI is still working on ChatGPT to get that caveat off the record. So relying on a tool for API testing that itself is in the testing and research stages is not a professional practice and can be disastrous in the long run. Testers' purpose of using ChatGPT to reduce the workload with the help of automation is not achievable in the near future either. Writing endless test cases to cover all the user flows and still ending up with errors is a major reality a tester has to face.
Testers won't have to deal with these problems if the test cases are built around real network traffic. This will not only cover every user flow but also make it impossible for a missed case to cause an error in production. Also, developers shouldn't wait until the testing phase to test each interface. Instead, they should do continuous testing in the CI pipeline itself to avoid the problems that come with end-testing.
HyperTest helps QA analysts and developers who want to use automation testing in more than one way by making the work of both developers and testers easier. It lets developers find and fix API problems at the same time they are writing code, so the problem can be fixed at its source. This approach efficiently ships the code bug-free without failing to cover all user flows. It builds test cases from real network traffic and automatically creates a regression suite that is also kept up-to-date on its own. It covers every user flow that teams might miss and reports errors that teams couldn't see before.
To learn more about the features that make our testing platform stand out in terms of functionality and reliability and to familiarize yourself with the various testing procedures we offer, please check out our website.