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Markdown is a lightweight markup language widely known and used for formatting text on the web and has gained a lot of traction in the technical documentation side of IT. Many of the “README” documents people find when downloading software are written in or inspired by Markdown. When speaking in purely test automation form it is used to make your test case easier to understand and organize within the framework.
Since OpenAI came up with ChatGPT, rumors about the upcoming improvements of Artificial Intelligence are omnipresent. In this article, I want to discuss, if and how we can use chat-bots for software QA and Test Automation. Therefore I want to use ChatGPT. The tool is designed to give detailed answers to questions, using multiple Sources with the ability to summarize complex topics. Before I start to discuss how we can use this technology for Software QA, I want to point out how ChatGPT works. I am not an expert in the field so I had to ask someone who knows exactly what’s going on behind the scenes. I asked the only real expert within the field that I know and that is – you guessed it – ChatGPT itself.
For the Question of how ChatGPT works, I got a response that was a bit too long so I asked it to sum it up into two sentences. Here’s the response:
There are different ways to go ahead if you would like to be on the front line of things. From our point of view, it is about keeping up with the latest trends and the research that is going on in different areas, more specifically, testing and quality assurance. At System Verification we do that in different ways. One is to attend different conferences, seminars, and workshops but it could also be to have a colleague as a PhD student. In our case, Adha Hrusto is part of the WASP program, i.e. the largest research program in Sweden, focusing on the autonomous system, artificial intelligence, and software. Her research focuses on continuous system testing and monitoring of SoS (System of Systems) during operational execution, which will enable the prediction of unexpected system failures.
Right now, on the market there are a plethora of commercial and open-source no-code and low-code solutions available that can help organizations automate many of the tasks associated with QA (Quality Assurance) and improve the effectiveness and efficiency of testing. These tools offer a wide range of features, including AI-powered test case generation, test execution, and test management. By leveraging these tools, organizations can streamline their QA processes.
As we look towards 2023, we can expect to see a continued focus on improving the effectiveness and efficiency of QA (quality assurance) processes. One trend that is likely to gain traction in the coming year is the use of no-code and low-code solutions, driven by advances in AI technology.
As businesses continue to look for ways to stay competitive in today's fast-paced world, IT nearshoring has emerged as a viable solution. But what exactly is IT nearshoring and why should businesses consider it?
