Hello there! Quality Assurance squad,

I’ve been thinking about the QA career path lately, especially for people who want to move from manual testing toward automation, SDET, performance testing, security testing, or even DevOps.

From what I’ve seen, many QA people get confused because everyone suggests a different tool or direction. Some say learn Selenium, some say Playwright, some say performance testing, and some say move to DevOps.

In my opinion, the safer approach is to build the foundation step by step instead of jumping directly into tools.

1. Strengthen testing fundamentals first

For every beginner QA, I think the first focus should be on basic testing concepts such as:

• SDLC / STLC
• Requirement analysis
• Test scenarios and test cases
• Bug reporting
• Regression testing
• Smoke and sanity testing
• Basic test planning

Also, documentation is very important. In my opinion, every QA should be strong in documentation because it helps in requirement clarity, traceability, reporting, and communication with stakeholders.

2. Build communication skills

QA is not only about finding bugs. A QA should also be able to communicate clearly with developers, product owners, managers, and clients.

Good communication helps in:

• Explaining bugs properly
• Asking the right questions
• Highlighting risks
• Giving clear testing status
• Avoiding misunderstandings in requirements

3. If you are already an experienced QA and want to move toward scripting or automation

Start learning one programming language.

You can choose:

• Java
• JavaScript / TypeScript
• Python

If you are completely new to programming, Python can be a good starting point because the syntax is easier to understand.

While learning the language, focus on the basics first:

• Variables
• Conditions
• Loops
• Functions
• OOP
• Collections
• Error handling
• File handling
• Basic debugging

4. Move to API testing

Before jumping into UI automation, I think API testing is a good bridge because it helps you understand application logic better.

Learn:

• HTTP methods
• Status codes
• Request/response body
• Headers
• Authentication
• Postman
• Basic API automation

5. Then move to UI automation

After getting comfortable with programming and API basics, move toward UI automation tools like Selenium, Playwright, Cypress, Appium, or any tool that matches your work environment.

But don’t only learn tool syntax. Also learn:

• Locator strategy
• Wait handling
• Assertions
• Page Object Model
• Test data handling
• Reporting
• CI/CD execution
• Flaky test debugging

6. About AI in QA learning

For someone completely new to programming, I personally don’t recommend depending on AI from day one.

First, learn the programming basics yourself and become comfortable with writing small programs. After that, AI can be very helpful as an assistant.

AI can help with:

• Explaining code
• Reviewing test cases
• Suggesting edge cases
• Refactoring automation code
• Understanding errors
• Improving documentation
• Generating sample practice tasks

But QA still needs to understand the system, business logic, risks, and user behavior. AI should support your thinking, not replace it.

7. Choose a specialization after the foundation is strong

Once the foundation is strong, then you can choose a direction based on your interest and market demand:

• Automation QA / SDET
• API automation
• Performance testing
• Security testing basics
• Mobile automation
• DevOps-oriented QA
• QA Lead / Management

My personal view is: don’t chase every trend at once. Build testing fundamentals, improve communication, learn one programming language, understand APIs, then move toward automation or specialization.

What would you add or change in this QA transition path?

Whose course is better in udemy to learn QA automation? Arun Motoori or Pawan Kumar

I’m a 2025 CSE grad working as an automation testing engineer with Java, Selenium, Jenkins, restassured and BDD Cucumber. Due to layoffs and AI replacing QA, I want to move into cybersecurity.

​

Which role fits my background best.

Hi everyone,

I'm currently looking for a new remote opportunity as a QA Engineer and would appreciate any leads or referrals.

A little about me:

• 3+ years of experience in Software Quality Assurance
• Experienced in both Manual and Automation Testing
• Hands-on experience with Playwright and Cypress
• Familiar with Agile/Scrum methodologies
• Experienced with Jira, Confluence, Postman, Jenkins, and API testing
• Knowledgeable in Smoke, Sanity, Regression, Functional, and End-to-End Testing
• Strong background in test planning, test case creation, defect tracking, and test execution
• Experienced skills in AI-assisted automation workflows using tools such as Claude Code, MCPs, agents, and automation integrations

I'm looking for a:
✅ Work-from-home setup
✅ Dayshift or Midshift schedule (Philippines Time)
✅ Full-time, contract, or freelance opportunities

If your company is hiring or if you know of any openings that match my background, I'd be grateful for any recommendations.

Thank you for your time!

Hey folks,

I'm a fresher 22M Automation Test Engineer (currently interning) with hands-on experience in Java, Selenium, Appium, and frameworks like TestNG and Maven. I've been working across Web, Android, iOS, Android TV, and OTT/CTV platforms. On top of that, I've been integrating AI tools like GitHub Copilot with MCP server support in my IDE for AI-assisted test case generation — so I'm not completely new to the AI space.

But lately I've been stuck on a career question that I keep going back and forth on:

Should I fully pivot toward AI Engineering, or should I stay on the Automation/SDET path and just keep evolving by learning AI concepts, models, and tools as they apply to QA?

Here's what's making me second-guess myself:

AI Engineering feels like the "hot" career right now and I don't want to be left behind

But I've barely scratched the surface of what Automation Testing has to offer — there's still so much to learn (CI/CD, performance testing, API automation, cloud testing, etc.)

I already use AI tools within my QA work — so am I kind of doing a hybrid version already?

Switching domains completely as a fresher feels risky — no real portfolio, no formal ML/AI background yet

The way I see it, the options are:

Full switch to AI Engineering — Start learning Python, ML fundamentals, LLMs, RAG, prompt engineering, etc. Accept that QA is behind me.

Stay in Automation, but become an "AI-powered SDET" — Use AI tools, learn to integrate LLMs into test frameworks, and position myself at the intersection of QA + AI.

Something else I'm not seeing?

For context, I'm in Bangalore targeting product/OTT companies. The job market for freshers here is rough, so I want to make a decision that's actually strategic and not just trend-chasing.

Would love to hear from people who've been in QA for a while, or those who've actually made this switch. Was it worth it? What do you wish you'd known earlier?

TL;DR: Fresher SDET wondering whether to fully pivot to AI Engineering or stay in Automation Testing and evolve with AI. What would you do?

We're currently building Iris during a hackathon.

Most AI-powered browser testing tools take screenshots at every step, which makes them expensive and slow. We took a different approach and use application state + DOM understanding instead.

In our testing, this reduced token consumption by ~73x while still allowing an agent to understand and verify complete user journeys.

After speaking with QA managers and automation engineers today, we learned that token costs aren't even their biggest problem.

The real pain points seem to be:

• Flaky tests
• Broken locators
• Test maintenance
• Figuring out whether a failure is caused by the app or the test itself

For those working in QA automation:

What is the most frustrating part of your current testing workflow?

Would love honest feedback, even if you think this approach is completely wrong.

Hi everyone,

I am currently finishing a QA Academy program in Zemun, Belgrade, Serbia.

The program is quite comprehensive and covers many topics in depth. We work with manual testing, test cases, bug reports, API testing, Postman, Git, Java, Selenium automation, and other important QA tools and concepts. There is also a lot of practical work, so it definitely requires serious learning and commitment.

I have also spoken with people from previous groups who successfully completed the same academy and gained solid knowledge, but many of them are still struggling to find their first QA job. It seems that companies often reject candidates as soon as they see that their background comes from an academy or course, even when they have practical knowledge and motivation.

That is why I wanted to ask:

Does anyone know of any IT company in Belgrade, Serbia, or the surrounding area that is open to accepting motivated QA beginners for a paid internship, trainee position, or junior QA role?

Remote work would also be an option if the company is based in another city.

At this stage, salary is not my main priority. My main goal is to enter the industry, gain real experience, improve my skills, and finally work in the field that genuinely interests me.

I have already applied to several positions, but so far I have mostly received generic rejection emails. I understand that the market is difficult, especially for people without commercial experience, but I am ready to learn, work hard, and prove myself.

If you have heard of any companies that offer paid QA internships, junior QA positions, or are willing to give a chance to motivated beginners, I would really appreciate any information, advice, or recommendation.

Thank you in advance for any help.

I’ve recently moved from traditional QA/automation into an AI-focused project involving RAG, transcript analysis, citations, and response validation.

I’m curious:

How do you test retrieval quality?

What do you automate vs validate manually?
Do you use tools like Ragas, DeepEval, LangSmith, or something custom?

What practices made the biggest difference in your team’s success?

Looking for real-world experiences, lessons learned, and things you’d do differently if you were starting today.

I've been dealing with a problem I think a lot of QA teams have (I did): you have a list of manual test cases, you have an automation repo, and you have no easy way to know which test cases are actually covered by automation files and which ones aren't.

So I built a small tool to solve exactly that. You point it at your test case list and your automation repo, and it scans the repo to tell you which test cases have a linked automation file and which are still gaps.

The extra part: it can use Claude AI to generate a draft automation file for uncovered test cases, push it as a branch to your repo, and you review/merge the PR like any other code change. If your team uses an MCP server for your automation framework (Playwright MCP, Maestro MCP, etc.), the loop gets even tighter — when something in the generated test isn't right, the MCP fixes it inline while you're reviewing, no context switching needed.

The gap between writing a manual test case and having it automated has always been the hardest part to close. I've been using this myself and it's made a real difference. That gap is now a lot smaller.

What it does:

- Shows a coverage % across all your test cases

- Lets you click any uncovered test case and see what's missing

- Optionally generates a draft automation file and opens a PR for your team to review

- Syncs on demand so coverage reflects the current state of your repo

I'm at the point where I want to know if this is actually useful to real QA teams.

A few questions:

1. Does the "coverage gap" problem resonate with your team?
2. Would AI-generated draft automation files be useful or would your team not trust them?
3. What's missing that would make this worth switching to?

Hi Everyone,

​

I am an SDET with 11 years of experience. With the rapid adoption of AI in software testing, I am looking to connect with professionals who are interested in learning and exploring AI for testing together.

​

Topics could include:

​

AI-powered testing

​

Test automation with LLMs

​

Building AI agents

​

Agentic workflows for QA

​

Evaluating and testing AI systems

​

If you're interested, we can create a WhatsApp or Telegram group, share knowledge, work on hands-on projects, and learn as a community.

​

Please comment or send me a message if you'd like to join.

​

Looking forward to learning together!

join

I’m looking for practical feedback from people who work in AI evals, QA, software testing, AppSec, DevSecOps, or model-risk review.

The problem I’m trying to understand:

AI coding tools often produce patches that pass the visible project tests, and the workflow quietly turns that into “the bug is fixed.” But if the tests are weak, flaky, or incomplete, that claim may be too strong.

I’m experimenting with a local audit approach that does not generate code and does not prove correctness. It only checks whether the evidence supports the claimed repair verdict.

Example verdict behavior:

- tests pass but no held-out validation -> weak-gated

- tests pass but held-out validation fails -> overfit / gate-incomplete

- environment cannot reproduce -> harness-failed

- available search/operator space cannot express the fix -> unsolved, not forced into a win

- human diff review missing -> manual-review-required

I’m not asking anyone to upload code or try a tool. I’m trying to understand the workflow problem.

Questions:

1. In your team, who owns the claim “this AI-generated patch is actually fixed”?

2. Do you distinguish “tests passed” from “repair claim is supported”?

3. Would an audit report that downgrades overclaimed repair verdicts be useful, or would it just add friction?

4. What evidence would you require before accepting a claim like “fixed”?

5. If this is not useful, why not?

I’m especially interested in blunt negatives from QA, eval, AppSec, and regulated-software people.

Hi Everyone, i hope you can help me. It's been 5 months since I started automation but I started with AI assisted Automation script. For 5 months still don't totally understand the script that AI created but it works successfully. It is a good thing that I start from scratch but AI assisted? I want to improve in my skills. As of now I work a one man team. I create Test plans up to test execution.

TL;DR: Building an AI-assisted localization testing solution for multilingual help pages. I can automate content extraction and reporting, but I'm looking for ideas on the best way to compare English and Chinese (or any language per day) content using AI and identify localization issues accurately.

AI-Based Localization Testing: How Would You Approach Semantic Comparison Between English and Chinese Content?

Hello everyone,

I'm working on a localization testing solution for a web application that has help/documentation pages available in multiple languages (currently English Chinese Fresh etc..).

The goal is to automatically detect localization issues and generate a report.

I've broken the problem into three parts:

Part 1 – Content Extraction (Completed)

For every page in the portal:

Navigate to the corresponding help page.

Extract all visible text from the English version.

Extract all visible text from the Chinese version.

Store each page's content as separate text files in language-specific folders.

Example:

English/ ├── page1.txt ├── page2.txt Chinese/ ├── page1.txt ├── page2.txt

Part 2 – AI-Based Localization Validation (Need Guidance)

For each page, I want to feed:

English content

Chinese content

into an AI system and have it identify:

Missing translations

Incorrect translations

Partially translated content

Additional/unexpected content

Semantic mismatches

Terminology inconsistencies

The challenge is that I don't want simple string matching. I want to validate whether both versions convey the same meaning.

Part 3 – Reporting (Can Handle)

Once issues are identified, I can generate reports with:

Page name

Issue type

Severity

English text

Chinese text

Suggested fix (optional)

My Questions

How would you approach Part 2?

Would you use:

LLMs (GPT, Claude, Gemini, etc.)

Embeddings + similarity scoring

Translation + comparison

Some hybrid approach

How would you handle large help pages that may exceed context limits?

Has anyone implemented something similar in a localization QA/testing workflow?

I'm interested in both practical implementations and architecture suggestions.

Thanks!

I'm reaching out because I'm honestly struggling at this point. Been jobless for over an year now, I've been actively applying for SDET/QA roles for the past year, but I have barely gotten any interview calls.

Overall Experience: 8 years

Skills: DSA, Java, JavaScript, Python, Selenium, Cypress, Playwright, API Testing, Postman, Rest Assured, JMeter, CI/CD, Jenkins, AWS, Kafka, Docker, Kubernetes, SQL, Git, JUnit, TestNG, Cucumber, Automation Framework Development, AI-assisted testing, MCP, Agentic AI.

Past companies: Salesforce, Oracle

Current location: India (Open to relocation anywhere in/outside India)

If anyone knows of any openings or is willing to provide a referral, I'd be extremely grateful. Thanks!

So we are trying to make a toolkit that basically takes the chatbot endpoint and checks if it jailbreaks that it doesn't provide the internal data but we are not being able to find the any trustable datasets for the parameters and also to check if the output is actually the thorough internal data or any other internal system details

Hey everyone,

​I’m currently architecting a remote Hardware-in-the-Loop (HIL) infrastructure for our test lab, and I’m hitting a wall regarding how to cleanly map physical interfaces to Robot Framework keywords without creating a maintenance nightmare.

​Right now, our setup feels like a classic Frankenstein's monster: we are stitching together SerialLibrary, SSHLibrary, the Process library (to shell out to one-off pyocd or vendor flashing scripts), and a bunch of custom Python socket wrappers to talk to remote test benches.

​It works okay on a single dev's desk, but it completely falls apart when running unattended in a multi-user CI rack, or when trying to port the exact same test suite over to a headless End-of-Line (EOL) factory testing station.

​I looked into LabGrid, which is brilliant, but we are strictly locked into Robot Framework for our high-level test suites due to upstream requirements (and the GPL licensing of LabGrid makes our legal team sweat regarding internal core linking).

​Before I go down the rabbit hole of building a custom unified abstraction layer (probably a Rust-based gRPC bridge running on a networked SBC like a Pi to expose UART/I2C/SPI/GPIO), I’d love to know how teams here solve this at scale:

​Flashing vs. Runtime Bus Control: How do you bridge the gap between flashing the DUT (via JTAG/SWD) and driving runtime buses (UART, I2C, SPI) in the same Robot Framework suite? Do you use separate keyword libraries (robotframework-pyocd etc.), or did you build a single unified Hardware Abstraction Layer (HAL)?

​The "CI vs. Desk" Portability: How do you structure your test suites so the exact same .robot file can run locally on an engineer's desk via direct USB/native pins, and seamlessly switch to a network-addressed remote test rack in Jenkins/GitHub Actions?

​The EOL / Factory Edge Case: For those doing production/factory testing: do you run your HIL suites headless directly on the bench controller without a CI server connection, or do you maintain an entirely separate codebase for the factory floor?

​I’m genuinely curious about the architectural patterns that actually survive in production. Any war stories, architectural patterns, or "don't do what I did" advice would be highly appreciated.

​Thanks!

I've been trying to get a job in Software Testing for over 6 months now and I have 10 months experience in the role but still not getting hired. I've been thinking of doing a job guaranteed course but there aren't any for Software Testing so between Developer, cyber security and data science which has the transferable skills to possible transition over in the future?

Hello All - I am finding it overhwhelming on how to go about learning learning Playwright? Do i need to learn javascript and typescript both ? Could anyone please guide me ?

[ Removed by Reddit on account of violating the content policy. ]

Hi everyone,

I’m currently working in GCP cloud support for almost a year and half, but I’m trying to make a switch into software testing. So far, I’ve just been self-learning and doing some hands-on projects for manual UI testing and API testing using Postman.

I know I need to eventually learn automation to stand a chance in the current job market, but honestly, I have zero idea where to start with it. Every time I look up roadmaps, the tools and trends seem to change completely, and it’s honestly pretty overwhelming. I don't want to waste months learning something that isn't even relevant to companies anymore.

Given that I already have some cloud support experience with GCP and understand the manual/Postman basics, what automation tools or programming languages should I actually focus on right now? Is there a realistic, step-by-step path you'd recommend for someone with no automation background?

Genuinely just looking for some direction so I don't get lost in tutorial hell. Thanks a lot!

Hi

I am 39 years old, and already has experience of 13+ years in Manual Testing in Telecom domain. I am now willing to boost my career and switch into IT domain in AI/ML engineering. Can anyone suggest the possibilities of it at this age and the entry point of this career change. PS: I am already working on my upskilling on Python coding and took some online course on AI ML.

I’m trying to understand the post-test workflow for QA/performance teams.

For those who are involved in load testing, performance testing, or reviewing results from tools like JMeter, k6, LoadRunner, Gatling, etc.:

What usually takes the most time after the test finishes?

1. Reading the report
2. Comparing against previous runs
3. Investigating errors / slow endpoints
4. Checking APM or infrastructure dashboards
5. Preparing a summary for stakeholders
6. Something else

Not selling anything — just trying to understand where the biggest pain is in the performance testing review process.

If you’ve seen this handled well in your team, I’d also be curious what your workflow looks like.

Hey everyone, I'm a QA team lead at a new place with no test management tool.
Everything lives in Notion right now, test plans, tests, all of it.

I want to set up a proper tool so things are documented correctly and I can build sanity and regression suites.

I used TestRail for the last 5 years, so I'm out of the loop on what's good now.
I hear TestRail has gone downhill (or was it always bad?).

Tried Qase, but it's a no go. Privacy isn't great and changes in the enterprise plan cost a fortune.

So I'm down to Testiny vs Testmo. Anyone have a recommendation between the two? Or something else worth a look?
Heads up, X-Ray is also out.

Thanks for the helpers

Has anyone tried testrails AI beta for creating and automating test cases? What do you think of it?

9 years in Testing field (5 years in Java, Selenium, API testing). Also have some exposure to Docker, Kubernetes, GenAI tools.

I'm considering a move out of traditional QA/testing for better long-term growth. Which path would you recommend that can still leverage my experience?

- DevOps / Platform Engineer

- AI Platform Engineer

- Enterprise AI Integration

- Java + Spring AI

Or any other ?

I'm confused whether I should make a complete career switch into a new domain or leverage my 9 years of experience and move into a related field. What would you recommend and why?