AI-Driven QA | Part 2: Challenges in Traditional QA and How AI/ML Can Solve Them

Hello again, everyone! In the first part of this series, we discussed how AI and ML are transforming the QA landscape and why it’s important for us, as QA engineers, to embrace these technologies. Now, in this second part, we’ll dive deeper into the challenges we face in traditional QA practices and how AI and ML are uniquely positioned to solve these issues.

Challenges in Traditional QA

Let’s face it — manual QA processes, though reliable in certain cases, are becoming less efficient as software development speeds up. Here are the key challenges we regularly face in traditional QA:

1. Repetitive and Time-Consuming Testing

A major portion of manual QA is repetitive testing, especially regression testing, which needs to be run over and over every time there’s a new feature or update. As QA engineers, we spend hours manually executing test cases, and this can slow down the development cycle.

For example, think about regression testing. Every time we introduce a new feature, we have to run the same tests to ensure existing functionalities haven’t broken. While critical, this task can be monotonous and resource-intensive, leading to bottlenecks in fast-paced…