{"id":8232,"date":"2023-03-20T08:21:24","date_gmt":"2023-03-20T08:21:24","guid":{"rendered":"https:\/\/www.kovair.com\/blog\/?p=8232"},"modified":"2025-09-29T11:14:01","modified_gmt":"2025-09-29T11:14:01","slug":"using-chatgpt-for-automated-testing-in-devops","status":"publish","type":"post","link":"https:\/\/www.kovair.com\/blogs\/using-chatgpt-for-automated-testing-in-devops\/","title":{"rendered":"Using ChatGPT for Automated Testing in DevOps"},"content":{"rendered":"\n
\"Using<\/figure>\n\n\n\n

That ChatGPT took the internet by storm on November 30th<\/sup>, 2022, would be an understatement. Within a week of its release, it had reached a million users and a staggering 100 million by January 2023. It has avid supporters and harsh detractors alike, but it most certainly presents new opportunities across multiple industries.<\/p>\n\n\n\n

Shortly after its release, it caught the attention of DevOps practitioners. It can create convincing-looking code, and its machine-learning capabilities seem to promise continued growth and refinements. With its seemingly endless possibilities, using ChatGPT for automated testing sounded feasible. But is it, and to what extent? Let\u2019s find out.<\/p>\n\n\n\n

What is ChatGPT?<\/h2>\n\n\n\n

If you happen to have missed the buzz around ChatGPT, a brief introduction to it should be in order.<\/p>\n\n\n\n

ChatGPT is a Large Language Model (LLM) on a chatbot interface developed by OpenAI. The \u201cchat\u201d component in its name reflects the chatbot’s interface, and the GPT acronym (for “generative pre-trained transformer”) reflects the LLM core.<\/p>\n\n\n\n

As it stands, ChatGPT pulls from over 175 billion parameters. It is also distinctly user-friendly, able to make sense of prompts and generate responses swiftly. In combination, these assets make it one of the most powerful and accessible AI language models to date.<\/p>\n\n\n\n

In the context of DevOps, ChatGPT can also write prompt-based code in multiple programming languages. It\u2019s this capability and promising early results that made users hopeful it could fully automate testing. AI seems to be the future of QA testing<\/a>, so the hope was reasonable.<\/p>\n\n\n\n

DevOps and MLOps<\/h2>\n\n\n\n

Indeed, DevOps and MLOps can \u2013 and already do \u2013 coexist and synergize. As we\u2019ve written before, machine learning is powering the next era of DevOps<\/a>. MLOps adds scalability, efficiency, and security to the common perks of DevOps.<\/p>\n\n\n\n

Their relationship, however, is synergistic. No LLM can likely revolutionize DevOps overnight, despite its vast capabilities. Indeed, ChatGPT is capable of creating a complex test automation pipeline with CI\/CD steps and bash code. It can produce well-documented code and can often make very accurate assumptions about the prompts\u2019 intentions. But as deep dives began, the initial expectations started seeming overblown.<\/p>\n\n\n\n

Using ChatGPT for automated testing in DevOps<\/h2>\n\n\n\n

With the above in mind, one cannot deny that ChatGPT does hold potential for DevOps uses. Any tool with such a massive scope of capabilities inevitably would. However, it\u2019s equally vital to note that ChatGPT is not without its quirks. These quirks are notable and often substantial. As such, they should be taken into account when exploring the limits of this tool.<\/p>\n\n\n\n\n\n

As with all emerging technologies, MoversTech CRM suggests embracing it \u2013 but cautiously so. Much like CRM technologies require years of evolution and polishing to reach their current, established form; they suggest that ChatGPT, too, will need its due time to reach maturity.<\/p>\n\n\n\n

The following breakdown of ChatGPT\u2019s perks and quirks as regards applications in DevOps should best illustrate this argument\u2019s validity.<\/p>\n\n\n\n

The good<\/h3>\n\n\n\n

Initially, ChatGPT does come with an array of capabilities for DevOps uses. Some of them include the following:<\/p>\n\n\n\n