How AI Could Revolutionize Software QA

January 14, 2025 2-minute read

How AI Could Revolutionize Software QA

The conversation around AI in coding often focuses on boosting developer productivity—streamlining UI creation, writing infrastructure configs, and automating unit tests. But the area I believe AI will impact the most in software development is Quality Assurance (QA).

Here’s why:

Traditionally, end-to-end UI functional testing has faced trade-offs:

Exploratory Testing: Great for consumer apps but difficult to replicate results or establish a reliable baseline functionality, so you have to mix it with manual testing.
Manual Testing: QA professionals create detailed test suites and execute them step by step for each release. While repeatable, it’s time-consuming for each test runs; I have seen test suites that requires 3 man-weeks to run.
Automated UI Testing Scripts: Efficient and repeatable when integrated into CI/CD pipelines. But maintaining these scripts is resource-heavy, with teams needing 1 test developer for every 2 - 4 software developers. Plus, even minor changes can easily break the scripts, adding to the overhead.

So it’s always a trade-off between being costly, or slow, or unstable, pick one problem.

Enter AI-Powered QA

AI advancements, especially in visual models, are changing the game. Now, automated end-to-end UI test scripts can be written in natural language. This drastically reduces the cost of maintaining test scripts, bringing it closer to the effort required for manual test cases. The difference? These scripts can run quickly (in minutes instead of days) for every single build or deployment.

Exciting Tools to Watch

Libraries like MidScene.js and Shortest.com are early examples, enabling developers to write natural language UI test cases and assertions. These tools showcase how AI can simplify QA workflows and reduce overhead.

The Future of QA

I foresee large software teams adopting this AI-powered approach, with new SaaS platforms and toolkits integrating testing pipelines. These tools could empower project managers to manage test suites more effectively, making automated QA accessible and scalable.