I only wrote a few testcases for Loginpage. It is just demo to give you an idea on how i write test cases.

Sheet1 was written after i did a bit of online research and got to know what the website is like.

Sheet 2 is how i started but stopped.

I can't share the test cases i officially wrote for the company i work at so this is all i can provide till i filter out company info and share those test cases.

Hey

I was wondering do the developers push work into your QA environment without checking with you first or are you in control of what goes into QA? So if work gets pushed do you have to approve it before it goes into the test env

Hello! I want to know how to improve the ticket effort estimation in my team. Currently, the devs put an estimate. I'd like to know the best practices for QA to contribute to this.

Should the devs and QAs estimate together? Should we just add on to it?

What are your best practices? Which estimation process worked and which ones did not?

We are a new QA team and part of my job is to improve the processes in my team. I appreciate your help. Thank you!

As the old moderation silently disapeared and the group did not have moderation anymore, i asked reddit to give me moderation, which they approved.

As first step i asked reddit to make the group public again, so everyone can post. This change for the group should be in action within the next 24 hours.

Please obey the rules, at one time i will refine them a bit, but as of know we will be working with the already existing ruleset. Depending on severity of rulebreaking there might be permanent bans without warning from me.

To make it less spam i changed posting rules so that new and low karma accounts can not post here.

I inherited a backlog of almost 1000 open admin actions, dont expect me to work them down fast.
But still please do report and downvote posts that do not follow the rules.

Whats my goal for this group? I aim for this group to be a exchange for quality content around software testing. I wont tolerate the 8105th "i made an AI tool" post or the 56481st "QA is dead" post. Maybe we will also disallow resumee feedback, there are other good subs for that.

I am open to suggestions on how to moderate this forum, about the rules and everything. Post your suggestions down there in the comments.

I am open and happy to build up a new mod team, 1 mod is not enough, 3 to 6 would be optimal.
If you feel like it, feel free to message me with your background in softwaretesting (short form) and why you wanna moderate this group. People with private profiles wont be accepted, sorry.

🟢Have a good time here☑️

I built FlakeHarbor, an open-source Rust CLI for analyzing historical JUnit XML test results.

It stores test runs locally in SQLite and reports:

- failure rates

- pass/fail transitions

- consecutive failures

- p50 and p95 duration

- recent regressions

- a documented flaky-test confidence score

The demo imports several included test runs, identifies a test that alternates between passing and failing, filters the results, and compares two reports.

Output is available as a terminal table, JSON, Markdown, and standalone HTML. Everything runs locally, without uploading CI results to an external service.

I would particularly appreciate feedback on the scoring model. Which signals should have more or less influence when ranking flaky tests?

GitHub:

https://github.com/NolanCotter/flakeharbor

Stryker-style mutation testing never worked for our E2E layer: mutating app code means rebuild + redeploy per mutant, which is a non-starter against a staging environment. But the question it answers ("would my tests notice?") is exactly what I wanted for a Playwright suite full of AI-generated specs.

The workaround that ended up working: mutate the assertion instead of the app. Mark the primary assertion of each test, auto-invert it, re-run the test. A test that stays green with its own assertion flipped is hollow. One inversion per test, no rebuild, cost is one suite run.

On our production suite it found no hollow assertions (63/64 killed, suite was in good shape) but it did flag a spec that had been silently skipped for months: skipped tests can't demonstrate a failing inversion, so instead of guessing, the tool refuses the verdict and says why. That refusal turned out to be the most valuable output of the whole run: "I could not check this" beats a green checkmark over a test that never executes.

Tool is open source: playwright-mutation-gate (npm).

Interested in how others handle trust in generated tests. Manual review doesn't scale when the generator writes faster than you read.

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.

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.

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.

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.

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?

Chasing total end-to-end test coverage usually means a team is substituting thoughtless metrics for an actual risk management strategy. In reality, it multiplies your maintenance burden and leaves you with a brittle, slow pipeline that everyone eventually ignores. We should be targeting critical business paths and high-risk user flows instead of trying to automate every possible click. Anyone else seeing this?

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

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

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

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??

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.

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

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?

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?

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?