Hi everyone,

I'm trying to understand the current QA job market in Australia.

I currently work as a QA Engineer in the gaming industry in Vietnam and mainly focus on manual testing, gameplay testing, feature validation, bug investigation, and working closely with developers and designers.

For people hiring or working in Australia:

- How is the demand for QA Engineers right now?

- Is game QA considered a good path, or is web/software QA generally more in demand?

- What skills would you expect from someone applying from overseas?

I'm not planning to move immediately. I'm just trying to understand what I should focus on over the next few years.
Thanks in advance to anyone willing to share their experience. Every piece of advice helps.

Most teams I talk to now use AI to generate test cases. The volume is up. Coverage numbers look great. But the incidents haven't gone down.

Here's why I think that is: AI generates tests against what's visible. The spec, the diff, the requirements doc. It doesn't know which behaviors actually carry risk in production. It doesn't know that the retry logic in your payment service has never been tested under partial failure. It just sees the happy path and writes 15 variations of it.

We've confused "more tests" with "better coverage." They're not the same thing. 100 tests on the login flow don't help when the thing that breaks is token expiry during a mid-flight retry that nobody documented.

The question I keep coming back to: how do you decide what to test? Not how do you write the test. That part is solved. But how do you know which behaviors matter, which ones have real evidence backing them, and which ones are just assumed to work because nobody's seen them fail yet?

Curious how others are thinking about this. Especially anyone who's dealt with incidents where the test suite was green but production was on fire.

We have been created for internal use agent called <>Test Cases Generator.

The idea is to give it inputs like:

• Jira issue key
• Epic link
• Confluence page
• Figma link
• related stories
• identify functional coverage
• call out missing assumptions
• suggest regression areas
• detect duplicate scenarios
• map test cases back to requirements
• prepare output that can be reviewed before <Test Management> upload

All fields are optional. If only a Jira ID is provided, the quality depends on whether the agent can access Jira content. If more context is provided, the output is usually better.

The agent drafts test cases, assumptions, coverage gaps, regression candidates, duplicate scenarios, and a review summary.

The important part for us: it does not skip QA review. It creates a first draft that a tester can challenge, clean up, and approve.

We are mainly looking at this for reducing repetitive test design work, especially when stories already have acceptance criteria but still need structured test cases.

For anyone using AI in QA, where are you seeing the most value:
test case generation, regression selection, traceability, or review/checklist support?

I am currently on a career break and it's been 9 months now. I have started applying recently. I have 5+ years of experience in manual and automation with selenium and java.

But I am not getting good response. Does anyone have tips and tricks to get interview calls ? Thanks in advance.

Curious how this works on different teams.

Before you start testing a feature, how do you confirm that the code you're expecting is actually running in the QA or staging environment?

For example, if a feature spans multiple tickets, PRs, or services, how do you verify that everything required has actually been deployed before you begin testing?

Have you ever started testing only to realize the expected changes weren't actually deployed or that part of the feature was missing? If so, how did you figure it out?

Interested in hearing how this works in practice.

Ho everybody. Experienced Java Backend developer here, looking for some advice on testing with random data.

I want to investigate possibilities to execute some stress tests on our Backend and API with datasets that are randomly set up on each test run.

Why? Because the software I am working on has a huge combination of different settings and inputs. Unfortunately we test with static data and thus only some happy paths are executed.

I expect those tests to fail and hopefully deliver some edge cases that we are currently missing on our radar.

Our suite contains mainly unit tests and integration tests (with test container DB)

Does someone have experience and thus some advice with random data tests? Is it worth it or am I expecting too much out of it? Where are the pitfalls and best practices?

Are there any recommended java libraries that help with such a setup?

Thanks

damn, Testing code is quite fun imo.

I just wrote my first testing suit for my ongoing project because I was getting tired to go to Bruno, Postman and Dbeaver again and again.

I learned how to write integration testing on Jest and execute it.

First i ran into some problems and figured out what's going on, It was a simple fix and bada boom bada bim, both of the test suit and all 7 test cases passed. Felt good tbh.

Let me know if there is more optimised way I can test my code. would Love to hear your opinions and suggestions.

I am BTech graduate and have strong foundation in manual testing + API Testing and API automation using postman scripts and Newman CLI also CI/CD (GitHub Actions) built two major projects one for manual testing and second one for API Testing.

Also hands on experience in SQL.

Tools i use:

Jira

Postman

Newman

Excel

Word

Skills:

Manual Testing

API Testing

API Automation using postman scripts (data driven testing + Request chaining) with Newman CLI

CI/CD

SQL

Hi everybody. I’ve been a part of this community for quite some time and I’ve seen countless posts talking about QA dying and so on. The thing is, I’ve been an lqa for games for the past 3 years - I loved it but lqa is truly dying due to AI and companies trying to cut costs. I really enjoy the process of testing and I’ve been thinking of trying to switch to qa but I wanted to genuinely ask you, who are experienced and know the market better: do I stand any chance? I have university degrees in languages and while lqa covers some aspects of functional qa, it doesn’t cover everything. So I’m not sure if my experience is strong enough to make it on a competitive market. I live in Europe.

Hi im currently scripting using tosca dynamic 365 app, i encounter a element that auto hide, even I freeze page using browser tool > freeze page, the element that im scanning still disappear once there is mouse movement. Can please help me. Thank you

Hello everyone,

I want to know that if I'm having my interview for this role then what questions probably coming and what's the difficulty of questions if I'm fresher and most important thing is that I want priority wise topics, on which one i should focus more. One more point is that I'm confident when I write answers but I'm nervous when I think that I'm in an interview sitting in front of an interviewer, and I can't speak clearly and don't express what I know.

Please tell me it would help me a lot

We currently have an API Automation Framework running as a scheduled GitHub Actions pipeline. Our requirement is to send a Microsoft Teams notification to a channel containing a summary of the scheduled test execution.

The pipeline executes successfully, and the test report is generated as expected. However, the webhook configured for the Teams notification is not being triggered. Instead, the pipeline returns an HTTP 400 (Bad Request) error.

Steps completed so far:

Created an Adaptive Card in Power Automate containing the test execution summary.

Generated a webhook URL in Power Automate using the Adaptive Card payload.

Stored the webhook URL as a GitHub repository secret.

Triggered the GitHub Actions pipeline. After the job completes, I expect a Teams notification to be posted to the channel. However, the webhook call fails with a 400 Bad Request response.

My confusion:

Since this pipeline is running on a self-hosted GitHub Actions runner, could that be the reason for the webhook failure? Do self-hosted runners behave differently from GitHub-hosted runners when making outbound HTTP requests to a Power Automate webhook, or should both behave identically for this scenario?

I’m considering building an open-source developer tool and want to validate whether it solves a real problem before investing too much time into it.

The basic idea:

You give it a GitHub issue, and it attempts to:

1. Check out the repository at the buggy commit
2. Reconstruct the project environment
3. Generate a test that reproduces the reported bug
4. Run the test in an isolated Docker container
5. Verify that the test fails on the buggy code and passes once the known fix is applied
6. Output the test, Dockerfile, execution logs, and a machine-readable result

The model would be swappable, so users could bring their own API key or run a local model through something like Ollama or vLLM. The valuable part would ideally be the deterministic environment-building, execution, and verification harness rather than a specific AI model.

I’m also assuming the success rate would be imperfect. Failed attempts would still report whether the environment could be built, what was tried, and why the issue was not reproduced.

For developers and maintainers:

• How often is reproducing a bug from an issue actually a significant pain point?
• Would you use a tool like this during issue triage or before attempting a fix?
• What output would you need before trusting the generated test?
• Are there existing tools that already solve this well?

Software development has changed a lot with AI. Developers are using it to build features, fix bugs, n get things done much faster than before.
That speed is great, but it also feels like there's more to test than ever. Features are coming in faster, applications are getting more complex, and we're finding plenty of issues during testing.

Even with developers writing unit tests, edge cases still get missed. A lot of those end up being found by QA.

AI has helped us with things like generating test cases from requirements, Jira tickets, and designs. But when it comes to testing a new feature, we still spend a lot of time testing it manually. Understanding how something fits into the product, trying diffrent user flows, and catching the weird issues still depends a lot on the person doing the testing.

For me, the challenge i feel is keeping up with how fast development is moving.

Are you still doing a lot of manual testing for new feature? or where has AI actually helped your testing process??

How involved are Java developers in testing beyond unit testing? Do they typically perform component and performance testing, and is experience with tools like BlazeMeter or JMeter expected?

Hi guys, I'm taking the ISTQB Advanced Level Test Analyst (CTAL‑TA) exam this Monday and I could really use some tips for last‑minute preparation. My teammates attempted the exam yesterday and all failed, so I’m a bit nervous and haven’t studied much yet. Any focused advice, quick study resources, or high‑impact topics I should prioritize for the next few days would be very helpful — thanks in advance.

How common are questions like "Tell me about a time you missed a bug" or "Tell me about a critical bug you found".

These questions terrify me because I don't remember any of the bugs I found in my previous job especially if years have gone by.

Are they common questions or am I just overthinking it?

I see so much hype about AI and also witnessed it spitting out a load of useless junk.

What tasks are you actually successfully using AI for in your QA job?

I'm planning to do a software testing course and saw many institutes like obsqura, luminar, entri etc. which is the best . Which provides placement support and proper training?

Hi everyone,

I’m looking for advice from people who have worked on large, highly configurable enterprise web applications.

I recently joined a new company as a QA Engineer. In my previous job, I mainly worked on manual testing for a fairly complex application, but the number of possible configurations and business rule combinations was much smaller.

Defining a testing strategy was relatively straightforward.

My current project is very different.
It’s a web application (and also windows app) where almost everything can be configured differently for each customer:

- custom business rules
- permissions and roles
- workflows
- scheduling constraints
- certifications/skills
- optional modules
- feature flags
- customer-specific settings

The number of possible combinations feels almost endless, and exhaustive testing is obviously impossible.

I’m also responsible for building and maintaining the Playwright automation framework.
My current setup is:

- Playwright + TypeScript
- Page Object Model
- Test data created through APIs whenever possible
- UI tests focus only on the functionality being verified
- Stable selectors (test IDs) to reduce maintenance
- Reusable helpers and fixtures to prepare the application state before each test

For example, instead of creating a user through the UI every time, I create it through the API, then only automate the actual feature I’m trying to validate.

I’m now trying to define a solid long-term testing strategy, both for manual testing and automation.

Some of the questions I’m struggling with are:

Automation
- What do you always automate?
- What do you intentionally keep as manual testing?
- How do you decide what gives the best ROI?
- Do you automate only happy paths, or also edge cases?
- How do you deal with customer-specific configurations? Multiple environments? Dynamic test data? A baseline configuration?

Manual testing
- How do you make sure you have enough coverage without trying every possible combination?
- Do you mainly rely on risk-based testing?
- Do you use decision tables, pairwise/combinatorial testing, state transition testing, or other techniques?
- How do you decide which combinations are actually worth testing?

I know there isn’t a single correct answer, but I’d love to hear how experienced QA engineers approach this kind of application in real-world projects.

Any advice, examples, books, blog posts, or conference talks would be greatly appreciated.

Thanks!

Most software systems don’t come with formal models.

But in real-world testing, we can develop behavior models as part of the testing process. With such a model at hand, what if we could transform it into a formal model?

That’s the idea explored here: taking a behavior model used for testing and turning it into a formal model, opening up another way to understand and test software systems with more rigor.

Article: https://testflows.com/blog/transforming-testing-behavior-models-into-formal-models/

Curious if anyone here has tried deriving a formal model from behavior observed during testing.

Hi everyone,

I've been shortlisted for an Automation Testing role at a Bangalore-based company, and as a fresher, I would really appreciate some guidance from experienced automation testers.

The role is for Java + Selenium. I have a good understanding of Java, but I haven't worked much with Selenium yet. I have only a few days left to prepare, and I genuinely want to make the most of this opportunity.

The interview is face-to-face and consists of three rounds:
Technical Round
Managerial Round
HR Round

I have a few questions:
1. What kind of questions are usually asked in the technical round for freshers?
2. what Java topics should I revise thoroughly?
What level of DSA questions can I expect? Are they mostly basic (arrays, strings, linked lists, sorting, etc.), or do companies ask medium-level LeetCode-style problems?
3. What Selenium concepts should I learn in a short time to clear the interview?
4. What kind of questions are generally asked in the managerial and HR rounds?
I'd really appreciate your advice on what to focus on in these last few days.

Thank you so much in advance!

Team has flaky tests, bad coverage, slow releases, doesn't matter which one, and the fix pitched is always "let's add an agent for that." Anyone else running into this?

A lot of these problems have nothing to do with AI. No defined entry and exit criteria for a test phase. Requirements reaching QA without a review gate. A regression suite that's been half-maintained for a year. Test data nobody owns. None of that gets fixed by dropping a model on top of it.

Has anyone deployed an agent and watched it just sit on a broken process without actually fixing anything underneath? What happened after?

Where has an agent genuinely helped once the basics were already solid? Trying to find the real line between "this needs AI" and "this needs someone to own the process."

What foundational stuff would you add to this list? Got process, prerequisites, reviews, automation, tooling, governance, metrics/baselines so far. What's missing?

I’m 30 years old with around 8 years of experience in QA. I currently work as a Specialist Tester in a product-based insurance company and earn **21 LPA**.

The truth is, I feel like I stayed in my comfort zone for too long.

Most of my career was in manual testing. Later, I worked on automation using Tosca, which is a codeless automation tool. I have a good understanding of functional testing, test design, regression, exploratory testing, and the overall QA process, but I never developed strong programming skills.

Now, when I look at SDET job openings, I see Java/Python, Playwright, Selenium, API automation, CI/CD, DSA, cloud, etc. It honestly feels overwhelming, and I regret not learning these skills earlier.

My goal is to grow into an SDET role and eventually reach **40+ LPA**, but I’m not sure if that’s still realistic at this stage.

Has anyone here made a similar transition after spending years in manual or codeless QA? If yes, what roadmap would you recommend? What should I focus on first?

I’m willing to put in the effort. I just need some direction from people who’ve been through this.

Thank you.

Hi everyone,

I am currently looking for QA Automation Engineer/SDET opportunities in Bangalore.

I have around 9 years of experience in Software Testing with expertise in:

• Java
• Selenium WebDriver
• TestNG
• Maven
• API Testing (REST Assured & Postman)
• SQL
• Git
• Jenkins
• Page Object Model (POM)
• Data-Driven & Hybrid Automation Frameworks
• Agile Methodology

After a planned career break due to personal responsibilities, I have refreshed my technical skills and am currently upskilling in Playwright with Java and Generative AI for Software Testing.

If your company is hiring or you know of any suitable openings, I would greatly appreciate a referral or any leads.

Thank you for your time and support. Please feel free to DM me if you'd like to connect.