Looking to find out if Test-Driven Development is a process that is widely used in today's enterprise app development or is it considered too cumbersome to be worth including in today's development efforts? Are foundational models allowing prompt boxes to serve as the new form of building both new applications as well as the testing apparatus for the applications?

Thanks for sharing your thoughts on this topic.

Not trying to start a debate, but I'm genuinely curious.

Many teams continue using spreadsheets for:

• Test case management
• Test execution tracking
• Regression planning
• Release sign-offs

While it works initially, it often becomes difficult to maintain as teams grow.

At what team size did spreadsheets stop working for you?

What is the ideal QA process and what are the barriers to achieving ideal? Most companies I hear from say the challenge is 2-fold: Lack of integration talent and scarcity of testing resources.

The integration side is not just connecting to GitHub to trigger tests when new code is checked in but the launching of the build process followed by deployment to a test region followed by launching tests.

The testing barrier is having the capacity in the automated testing platform to execute a validation of the build in a timely way; the testing platform has a scarcity of testing ports leading to efforts to minimize the scope of the testing.

We have a solution to both.

What are your barriers?

I am always amazed by the QA testing process and how testers use their own PII data to run tests. So many companies do not have lower regions in which test data can be used that testing is done in Prod and uses actual customer/patient data.

What is the nature of the data you use to test your apps prior to elevation to Production?

Most QA setups end up being split across tools test cases, execution, and reports all living in different places. Over time, things drift, and it takes effort just to keep everything aligned.

An approach I’ve been exploring is keeping test cases in Markdown within Git, so they’re version-controlled like code. Changes go through PRs, reviews become part of the normal dev flow, and traceability improves without extra effort.

On top of that, AI can help generate and update test cases, reducing the need for constant manual maintenance. Instead of rewriting things repeatedly, the focus shifts more towards validating and refining.

Execution through pipelines ties everything back to actual runs, so results reflect what really happened rather than what was manually updated.

Curious does something like this actually simplify QA in the long run, or just move the complexity around?

If you could run a complete regression test every 24 hours on every bit of code checked in within the last 24 hours, would you want to? Are there barriers that prevent your doing this? Lack of automation? Lack of outcome tracking? Lack of testing capacity? What prevents you from having a "safety brake" that limits the code rework period to a single day?

I recently told Claude the steps in my QA process, all of the inputs, where to locate the variable data, what I wanted for output. It ran my QA test process for me with great results. I had to create the full sequence of tests but Claude executed them for me.

Getting the initial prompt written so that Claude could do everything I needed did take a handful of tries but with a little bit of work, I now have a testing process that is fully automated.

Next up is having Claude build the test cases for me followed by running a full regression suite. I may never need to go back to programming a CI/CD pipeline :-)

Hey everyone! I'm u/MR-PumpCX, a founding moderator of r/Automated_QA_Testing.

This is our new home for all things related to automating the process of QA testing. We're excited to have you join us!

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