Welcome to the 302nd issue!

Wondering how to leverage AI to future-proof your career as a tester?

Today, I'd like to highlight two intriguing resources to help you understand it better:

• AI in Testing — What Should I Learn to Stay Future-Ready?
• AI and Testing: Personal Marketability by Jeff Nyman

Happy (AI) testing! 🙂

Dawid Dylowicz

In this four-part overview, Andrii Cheparskyi shares some good, practical advice on how to effectively use AI in testing. You can read more about ChatGPT & Claude Features, the AI IDEs and MCP for testers.

The notion of everyone being responsible for quality has been widely popularised in agile. But James Bach challenges it, recommending replacing it with personal responsibility and clear roles.

Moreover, Julia Kocbek shares interesting thoughts on How to solve dilemmas as QA.

AI is here to help us and we need to learn how to use it effectively. Medhani Ranaweera points out several areas in which she found it helpful.

And if you're one of the late adopters, you may resonate with Dennis Martinez's experience of going through My Five Stages of AI Grief.

Arik Aharoni explains how testing gaps accumulate over time and can hurt in the long term. It's a good reminder to regularly review test debt and risks.

In that context, Jitesh Gosai tells us how we can leverage Learning from failure: spikes, POCs, prototypes and MVPs.

Well said and reasoned, Patrick Prill! While AI can help us with some tasks, we shouldn't let it completely loose and detach too much from what it's doing.

Similarly, Katja Obring wrote about the importance of treating Review as analysis, not authority, when working with AI.

And all this makes people wonder — Is manual QA making a comeback due to AI?

Shrinath Rao shares a high-level overview of their agentic AI approach to test automation. I only wish we could see some examples of that, too!

At the same time, Bart Vanherck points out Why AI won't save your Flaky Tests.

Sometimes, test data is as important as the test design itself. Sudhakar Reddy Narra gives several tips on how to approach synthetic test data generation and what to be mindful of.

One common gap in automation is partial failures. Irfan Mujagić shows examples of simulating contract violations to check whether the rest of the app still works.

Want less noise from flaky suites? Rodrigo Alves Costa explains why you should focus on fast unit and integration feedback early and run broader system tests only after merging into the main branch.

However, Amrinder Singh Mallhi worryingly notices The Lost Art of Dry Running in today's software development.

Appium has recently upgraded to version 3+ and Hakan Tektaş describes all the changes and requirements for migration.

Now that Jira and Confluence have Rovo AI built in, there's an opportunity to use it for improving software testing processes. Parul Kaushik explains the potential use cases.

Also, wondering whether it's worth Using Claude code to understand code before testing?

Hrvoje Milković describes how they approached and what they learned from load testing an MCP server using k6.

And if you want to learn more, Millan Kaul gives a quick recap — What is MCP?

There's a lot we can learn from Tim Lolkema's approach to setting up AI agent workflows for generating Playwright tests.

One of the topics that often yields different opinions within the testing community is test case management. Is it needed? Here's what Brad DerManouelian thinks about it.

I don't think an introduction is required for this one. Just tune in and enjoy a fine conversation about the essence of testing between the testing legends — James Bach and Michael Bolton — invited by Alex Khvastovich.

The release ride... 😂

Thanks for reading!

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Dawid Dylowicz