"How generative AI changes testing and SDLC" - A.Kerimovs

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  Solution

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  Tests

Inspiration

Right now, I'm working in an American startup. Since I'm in charge of the product and often deal with development issues, I've been looking for AI-related solutions to simplify, automate, and improve testing. While exploring the hackathon tracks, I came across testRigor. This made me think about the potential of generative AI and how it could change the testing process and the whole Software Development Life Cycle (SDLC). I'm interested in artificial intelligence, including machine learning and NLP. So, I decided to participate in the hackathon focusing on generative AI!

What it does

I wrote an article discussing how generative AI changes testing and the development lifecycle. Through stories from around the world, I have illustrated examples of how various companies — from small startups and non—profit organizations to large enterprises - can potentially use a tool called testRigor to transform their testing and development processes. The article contains over 4,000 words and is supplemented with visual graphics suitable for publication, printing, and distribution.

How we built it

To make this article, I first dived deep into understanding generative AI. After that, I checked out testRigor's tools, made an account, and created various test scenarios. This helped me to understand how testRigor works. I also watched videos made by the founder to see how he talks about his product and how he explains functionality. This gave me a solid foundation. I brainstormed ideas and started with the article's content, examples, and visuals. That's how I came up with this work:)

Challenges we ran into

Since I had not worked with products similar to testRigor before, there was difficulty with the syntax for writing various test scripts and commands, and I had to refer to the documentation constantly :( I want to note that it was beautifully written and helped me in many ways! Also, according to the requirements, the article should be 2500 + words, which is quite a lot, while it should be interesting to read and constantly keep the reader's attention. There was difficulty in coming up with an approach that would allow the creation of such an article. But in the end, everything worked out.

Accomplishments that we're proud of

I'm proud that I could thoroughly study the project's topic and understand how the company's product works in the track I'm participating in. I managed to write good test scenarios, and of course, I'm glad I submitted the project on time. I'm also proud that I can finally get some sleep, seriously!

What we learned

I improved my knowledge of various testing options, figured out how generative AI works in testing, learned to write exciting and, most importantly, informative articles, and improved my skills in Figma.

What's next for "How generative AI changes testing and SDLC" - A.Kerimovs

The logical next step for me would be to introduce such an approach to development and testing in the company I am working in, first on one team and then on the entire product. I would also like to study the internal process of creating such tests in more detail and depth and perhaps write some research articles in this direction.

Built With

• figma
• google-docs
• grammarly
• openapi
• peerj
• pitch
• research-platforms
• testrigor