How I'm leveraging AI to reduce repetitive work in Software Testing.
Over the past few weeks, I've been building an AI-powered testing assistant using Model Context Protocol (MCP).
The goal wasn't to replace QA engineers.
It was to eliminate repetitive tasks so testers can focus on finding bugs, validating business logic, and exploratory testing.
Here's what I built.
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Over the past few weeks, I've been experimenting with ways to make software testing more efficient using AI.
The objective wasn't to replace QA engineers or generate test cases with a prompt.
It was to reduce the repetitive work that consumes a significant part of a tester's day.
By combining AI with product knowledge and controlled access to testing capabilities, tasks like understanding the application, preparing test data, and creating validation scenarios become much faster and more context-aware.
The biggest benefit isn't writing more automation.
It's giving engineers more time to focus on exploratory testing, critical thinking, and improving software quality.
This is something I've been building recently, and I'll be sharing the complete architecture, implementation, and lessons learned in an upcoming thread.
I'd also love to hear from the community:
What's the most repetitive task in your testing workflow that you'd like AI to handle?
#QA #SDET #SoftwareTesting #AI #AutomationTesting #GenAI
Heavy rainfall caused the Patalganga River to overflow.
β’ Videos circulating online appear to show multiple LPG cylinders floating in the floodwater.
β’ Reports suggest cylinders may have been washed away from a storage facility, but authorities are yet to confirm the exact number.
If these reports are accurate, the bigger question is:
How did so many gas cylinders end up in a flooded river?
Were adequate flood-risk assessments done?
Were safety protocols followed?
Was the storage facility prepared for extreme weather?
Natural disasters can't always be prevented.
Negligence can.