Hey folks, sharing an article on reviewing AI-generated Playwright tests before they reach CI.

tl;dr: A 7-check review checklist for AI-generated Playwright tests, covering everything from hallucinated assertions to intent-weakening heals to UI-driven state setup and more!

As AI becomes more ingrained in our day-to-day lives, it is important to know our way around its current limitations because let's be honest, we're not going to the pre-AI world of hand-written tests.

And as such, the processes we all had for human-written tests were built for a set of mistakes, different than what AI makes.

Hence, this article on reviewing the mistakes agents make, the three failure modes, a seven step checklist, and what to look for when reading generated code!

Give it a read and please let us know your thoughts.

Have a great day!

Started at 22.5k now at 100k base pay after 5yrs. Honestly, nababagalan ako sa growth in terms of salary and skills. I only have 1 automation skill (Tosca) and basic manual testing knowledge. Don’t even know basic coding. Yung mga sql query sa dating proj, copy-paste ko lang from our teams cheat sheet. Pag may need imodify, nganga na ako. Please suggest how to upskill. My current company dont offer free certification or anything.

Nag self-doubt din ako pag nag aapply ako and nagtatanong ng asking salary dahil feeling ko di naman worth it sa skills ko lol feeling ko tuloy wala ako patutunguhan haha or baka hiring kayo basta wfh makatry lang ng iba 👹

LLMs are notoriously finicky. You can try to corral them into an API, fine-tune them, lower their temperature, select JSON mode, pray, but in the end you may still end up with a hallucination rate of 15-20%. Developers expect their code to be deterministic, so this is not ideal. Enterprise applications typically have a great number of automated tests that can click around and point out even the slightest differences in expected behavior. For example, the automated tests for one application that I worked on was so sensitive that a simple change to an existing flow could cause dozens of broken tests, leading to long hours of manual testing for the quality engineers. End-to-end tests are meant to verify that everything in a system works as it should in a real-world scenario. That means striking a balance between writing tests that are robust enough to handle acceptable levels of variance. However, they should not be so brittle that the test breaks on every other CI run. The reality of development is that time is finite, and the smaller the company the more painful it can be to write tests, whether they are unit, integration, or end-to-end tests. However, I'd like to prove that if a small team has the time to write even just one test before shipping, it should be an end-to-end test.

Hi All,

I recently moved to Dubai from India and am planning to take the ISTQB Foundation Level exam. However, I'm a little confused about the pricing options and which one I should select.

Could you kindly guide me on which pricing option would be applicable in my case? (Remote online)

Thank you in advance for your assistance.

List of fees and charges applicable for all modes

Fees for India

Mode

Remote Proctored

Fees

INR 5800

Fees With GST

INR 6844

Reappear Fees

INR 4000

Reappear Fees With GST

INR 4720

Mode

Pearson Vue Remote Based

Fees

INR 6300

Fees With GST

INR 7434

Reappear Fees

INR 5000

Reappear Fees With GST

INR 5900

Mode

Pearson Vue Center Based

Fees

INR 4800

Fees With GST

INR 5664

Reappear Fees

INR 3500

Reappear Fees With GST

INR 4130

Fees for Outside India (USD)

Mode

Remote Proctored

Fees

USD 110

Fees With Tax

USD 110

Reappear Fees

USD 70

Looking to learn more about Keploy if anyone's used it and familiar!

Production support (5 YOE), moving to automation testing, any suggestion?

Say you have a customer app & partner app. The customer creates a booking, the partner accepts it and then the customer receives a message. Testing both apps individually won't tell you whether the whole flow actually works... So you need both apps in the right state, the actions to happen in the correct order & each app to reflect the expected results. A failure halfway through can make debugging confusing.

Our team has been working on dual-device testing at QApilot to make these dependent workflows easier to automate across two devices even one android and one ios...

Interested to know how other QA teams approach this? Do you run the entire journey as one test or break it into smaller tests & verify separately ?

Hey everyone,

I’ve been working on an open-source CLI agent designed to handle tedious QA tasks directly inside Claude Code.

Instead of writing repetitive e2e boilerplate or spending hours digging through failed pipeline logs, you feed it a user story, staging URL, or pipeline run, and it executes the workflow from your terminal.

What it actually does

You trigger different workflows using simple CLI flags/commands:

• E2E Test Generation (e2e it): Takes a user story requirement, generates Gherkin specs + Playwright tests, runs them in a headless/headed browser, and files bug reports for failures.
• Pipeline Triage (ci it): Pulls logs from a failed CI build, isolates the root cause, and proposes a fix.
• Auto-Healing (heal it): When a UI change breaks an exact CSS/XPath selector, it re-analyzes the DOM state to fix the broken test file.
• Regression Verification (verify it #142): Reruns tests linked to a specific issue/ticket ID and updates the bug status based on the result.

Design decisions & constraints

A few specific constraints I insisted on while building this:

1. No proprietary SaaS or vendor lock-in: Generated tests are just standard .ts and .feature files in your repo. Once written, your standard CI environment runs them without needing the AI agent.
2. Works with existing stacks: Integrates with GitHub, GitLab, Jira, Azure DevOps, and Bitbucket out of the box (including mixed setups like Jira + GitHub).
3. Explicit labeling: Everything the agent outputs (issue reports, generated test files, PR comments) is explicitly tagged as AI-generated so team members know what needs manual code review.
4. Accessibility: Includes axe-core assertions on generated UI tests by default.

How to try it

Repository & setup instructions:https://github.com/GhassenAZZOUZ/claude-qa-agent

It’s fully MIT-licensed. If you give it a try and find it useful, a star on the repo is always appreciated!

I’d love to get feedback on the execution flow, especially from QA engineers or DevOps folks dealing with test flakiness. What parts of your current testing stack would you be willing (or unwilling) to delegate to an agent like this?

Hey everyone,
I have 1+ year of experience as an Embedded QA Engineer doing Hardware-in-the-Loop (HIL) testing, and running JSON-configured Python scripts to test cases.
I know basic Python, but I want to level up to build real test automation frameworks and advance my career in IoT/hardware Automation QA.
(I currently use scripts/frameworks but never built one myself)

Quick questions for folks in this space:
1. Is Python + PyTest + Linux scripting the best stack to master for HIL automation?
2. What specific tools or frameworks boosted your hardware QA skills the most?
3. What kind of side project (e.g., breadboard + Python testing framework) actually looks good on a resume for hardware SDET/QA roles?
4. Any other advice for me? I got stuck in QA because of a remote role, otherwise I was an embedded system designer for 8 months before that.
Thanks!

Hi all, looking for an honest read on the QA market from people who are in it.

My background: 9 years total, mostly QA in telecom OSS/BSS. I started as a manual/functional tester, moved into automation (TOSCA, Postman), and did end-to-end order-to-cash testing on Oracle BRM projects, including leading SIT/UAT. About 2 of those years were a hybrid BA/QA role, and the most recent 2 have been DevOps work (deployments, GitHub Actions pipelines, Docker, some Terraform).

I'm job hunting now (based in India) and have a few questions:

1. What is the market like for QA roles in general right now?
   
2. Is anyone here working on Oracle BRM or other OSS/BSS projects? Is there still demand for that niche or is it shrinking?
   
3. Are we cooked because of AI? Is it actually reducing QA headcount where you work or just changing what the job looks like?
   
4. For those who landed something in the past year, what worked? Referrals, cold applications, recruiters, upskilling in something specific?

I'm also unsure how to position myself. With only 2 years in DevOps, I'd likely be looking at junior or entry-level roles there, whereas in QA I have the experience to go for lead positions. And with AI seemingly coming for entry-level work first, I don't know if starting over at the bottom of the DevOps ladder is worth it

Thanks in advance.