Hey everyone,

I’m currently exploring QA Automation Testing as a career path in India and wanted some realistic insights from people already working in this field.

I come from a development background and I’m considering shifting toward Automation QA/SDET because the generic web development market feels extremely overcrowded right now for freshers.

I wanted to know:

* How is the current job market for QA Automation Engineers in India?

* Are companies actively hiring freshers/juniors for automation roles?

* Which tools are most in demand right now — Selenium, Playwright, Cypress, Appium, etc.?

* Is manual testing still required before moving into automation?

* How difficult is it to switch from QA Automation to DevOps or SDET roles later?

* What salary range is realistic for freshers and after 2–3 years?

* Which skills actually help candidates stand out in interviews?

* Is this field still a good long-term career in the AI era?

Would really appreciate honest advice from people currently working in testing/automation/SDET roles.

Thanks!

Hey everyone,

I’m working on Agent Skill for software testing and standardizing the testing process.

I’d love to learn from tester here: if you had experience with something like this, would it be useful?

The idea is to let teams create reusable “skills” for testing workflows. For example, skills that can:

• Run exploratory testing flows
• Generate or update test cases
• Connect with tools like Playwright to generate test scripts

I think the skill would focus more on workflow and tool integration.

I’m trying to understand whether this is something QA teams would actually want to use, and where it would be most useful.

What does your testing process actually look like before you hit publish?

Because from what I've seen it's usually just "tested it a hundred times myself, first user found a crash in 30 seconds" and honestly fair enough when you're wearing every hat at once.

The problem is the bugs that get you are never the obvious ones. It's always that one weird screen transition that only breaks on a specific Android version, or a flow that somehow only fails when a real user goes through it in a way you never thought to try.

So where does it usually go wrong for you? Is it device coverage, not enough time to test properly, or something else entirely?

Hi everyone,

I’m looking for some advice on how to properly perform performance testing for dashboards specifically on Tableau Cloud. Since we don't have server-side access, I'm trying to figure out the best approach for two specific goals:

Finding accurate Load Times: What is the best way to measure how long a dashboard takes to fully render for an end-user?

Stress Testing: How can I simulate and test concurrent user hits to see how the dashboard holds up under heavy traffic?

Are there specific tools or built-in Tableau Cloud features (beyond the standard Performance Recorder) that you recommend for this?

Thanks for the help!

Hey all!

I'm looking for a couple mobile testers/QA in San Francisco able to put in a some hours a day in-person and test an AI-native Android app.

Part-time/Contract, great rates, and awesome to work with.

AI-native and great opportunity.

Let me know!

Ovi

When you're testing APIs, you'll often need to verify that two responses match
— whether you're comparing staging vs production,
checking for regressions after a deploy, or validating that a refactor didn't break anything. Here are three practical methods.

Method 1: Online diff tools

Paste both JSON responses into a web-based tool like JSONDiff or Diffchecker. The tool highlights added, removed, and changed fields with color coding.

Pros:

• Zero setup, works instantly in the browser
• Visual side-by-side view is easy to scan
• Good for quick one-off comparisons

Cons:

• Not safe for sensitive data (you're pasting into a third-party site)
• Manual process, can't be automated
• No way to ignore fields like timestamps or request IDs
• You still have to call both APIs yourself before you can diff

Method 2: Custom JavaScript script

Write a small Node.js script that fetches both endpoints and runs a deep comparison using libraries like lodash, deep-diff, or json-diff.

Pros:

• Full control over what counts as a difference (ignore timestamps, IDs, etc.)
• Repeatable and automatable — drop it into CI/CD pipelines
• Keeps sensitive data local
• Handles unusual ignore rules cleanly

Cons:

• You own all the maintenance — every edge case means more code
• No UI for non-developer teammates
• Bulk testing (e.g. 200 rows of CSV input) becomes a project of its own
• Ends up reinventing what API clients give you for free (env switching, secrets, request templating)

Method 3: Postmate Client

Postmate Client is a lightweight REST API client built into VS Code with response comparison as a first-class feature. It calls both APIs in parallel, runs the diff, and shows only the fields that differ in clean JSONPath notation. Supports both single comparisons (Data Row mode) and bulk comparisons via CSV/data tables.

Pros:

• Sends both requests for you in parallel — no juggling terminals or tabs
• Shows responses side by side with differences highlighted
• Bulk comparison via data tables is built-in, not a DIY project
• Environments and secrets handled via dropdown — no code changes
• Runs locally, so payloads stay private
• Results live inside your collection, easy to share

Cons:

• Requires installing the VS Code extension and learning the workflow
• Tied to VS Code as your editor

Hello fellow QAs, do you guys tested LLM based application like chat interface where we have to validate, evaluate LLM responses? I am looking for any tools that helps to inject prompts, or evaluate responses, correctness, hallucinations etc scenario. If any free tools please let me know Thanks in advance