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Author: Ujjwal Kumar Singh, Senior QA Engineer · 8+ years in enterprise QA  · beinghumantester.github.ioKey takeaways:Why a test run can pass every check and still hide a real defect between connected systems How experienced testers turn an unexpected observation into a hypothesis and a deliberate experiment Why searching for explanations differs from searching for actions, and why more automated actions don't replace investigation How the six-step investigative loop lets testing change direction as evidence builds upImagine an order flow that spans two systems: a web storefront that takes the order, and an ERP that owns the stock. Two customers buy the last available unit at almost the same moment. Both orders are confirmed. The storefront's checks pass, and so do the ERP's. No exception is raised. From the perspective of an execution-oriented automation system, the test has succeeded and there may be nothing left to investigate.An experienced tester may see something different. There

AI test engineering | Master vibe testing, AI tools, and agents by Karthik K.K. TL;DR/ Key Takeaway:Learn foundational AI concepts for QA, including AI, machine learning, and large language models (LLMs). Explore prompt engineering and context engineering techniques for more effective AI-assisted testing workflows. Use AI tools to extract insights from requirements, specifications, and meeting notes. Understand vibe testing and how AI-assisted workflows can support test design and execution. Explore popular AI tools used in testing and software development, including IDE copilots and CLI assistants. See how AI can support test automation, code analysis, and maintenance activities. Learn how MCP servers connect AI tools with real systems for UI, API, database, and BDD-related workflows. Understand how AI agents extend LLM capabilities through interactions with files, repositories, browsers, and other tools. Explore custom agents and reusable skills that can help standardize testing work

Your instructor Dave Westerveld:I have many years of experience in testing various types of software. I am based in beautiful Ontario, Canada and I love learning new things, and sharing what I’ve learned with others. I have worked in several different industries ranging from scientific computing systems to enterprise grade web sites. I love solving automation problems and diving deep in the technical side of testing. I have helped develop test automation frameworks and am proficient with using tools like Python to enhance my testing abilities.One of my goals is to help testers stay relevant in a constantly changing industry and so I make courses and other content to help testers stay up to date and progress in their careers. I occasionally blog here as a way to learn new things and share with others some of the skills I’ve learned.  Links to more info about me:LinkedIn Profile  / Personal Website  /  Email: davewesterveld@gmail.comDescriptionA free tutorial series for testers who want 

Generative AI for TestersBy Rahul ParwalIn this on-demand Generative AI for Testers session, you'll learn how to confidently apply Generative AI in software testing and quality engineering.TL;DR/Key TakeawaysLearn the fundamentals of Generative AI for software testing, including what AI can and cannot do within QA workflows. Understand Large Language Models (LLMs) in practical terms, including tokens, training data, APIs, and how AI-generated outputs are created. Explore a proven GenAI framework for testing that covers: AI as an assistant Experimentation and transformation Coding and test automation Learning and summarization Apply GenAI to: Test case generation Test data creation Requirements reviews Risk identification Bug report authoring Test documentation Improve test automation productivity through AI-assisted code generation, refactoring, debugging, modularization, and rapid prototyping. Master prompt engineering for testers using practical prompting techniques that im

AI Workspace can issue an MCP token, which connects your coding agent to it directly — Cursor, Claude Code, or Claude Desktop. Go to your profile, create a token, set an expiry of up to a year, and paste the configuration into your agent's MCP settings. Your agent then sees the available tools, including workflow execution.That means you can ask a quality question from where you already work. Point it at the functionality you just built and ask what test coverage exists; if the answer comes back empty, the agent knows to call a workflow that creates the cases. Workflows can reach qTest, Tosca, and NeoLoad. This can be used for the effective local testing for each engineer, but with consistent test repository.Two things to know before you start. The service accounts a workflow uses to reach your repository and your Tricentis projects are configured in AI Workspace separately, by an admin — you can mint a token without them, but workflows won't run.   

The qTest MCP Server finds objects by name — requirements, test cases, runs, releases, suites, cycles, builds and defects. Give it the project ID once and it carries that forward. After that you're naming things: show me the Homepage module, get me defect DF-12.The bigger unlock is what happens after retrieval. Search gets you candidates; the agent reads them. Point it at one module, have it list the test cases, then ask which ones touch the invoice screen — it'll read the descriptions and steps and tell you. Narrow the scope, let the agent interpret. Faster and more accurate than asking for everything in a project.For execution questions, skip search. Ask for an execution scope summary on a release, cycle or suite: structural tree plus run statistics in one call — passed, failed, never run. Then pull the latest test log for any run, with step-level results and linked defects.Writing works too. Create requirements, update test cases, link cases to requirements, create defects from a fa

AI-generated test cases are fast but imperfect. The typical response is to have a human review them: slow, manual, and easy to skip under deadline pressure.A better pattern is to build a validation workflow in AI Workspace that acts as a structured reviewer before anything reaches execution.An agent step checks generated test cases against criteria you define, such as coverage completeness, naming conventions, and presence of expected assertions. Anything that fails gets routed back: a comment on the Jira ticket, a flagged test in qTest, or a hold at an approval step where a reviewer signs off before the workflow continues. It's the QA equivalent of a linter, triggered on a schedule or by webhook as part of the workflow.You configure it in AI Workspace using a template or the Workflow Assistant. Describe the review you want in plain language, connect Jira or qTest through an API toolset or MCP connector, and add the approval gate. No code required. 💡Read more:Orchestrating AI-powered

Most teams run their agentic workflows manually — someone logs in, picks a workflow, hits run. But AI Workspace workflows are event-driven> Jira, GitHub, Azure DevOps and ServiceNow connect natively. A ticket transition or a merge starts the workflow the moment it happens.A PR merges to your protected branch → the GitHub integration fires the event → Agentic Regression Automation runs → your approval gate holds the output for review,  or waves it through if you've tiered that step as low risk. 💡Try it: pick one workflow you run by hand, connect it to a single event — a transition into "In Review" works well — and leave the approval gate in place. See where the output lands before you widen the trigger. Read more:Get Started guide AI Workspace: the control plane for autonomous quality engineering

What you'll learn:Why similarly skilled testers end up with very different levels of influence, and how the questions you ask reveal which path you're on Why strong execution stops being enough with experience, and what the organisation starts expecting instead How being the go-to expert for hard problems can make you valuable without making you influential — and what real leverage looks like Why leadership is about ownership and influence rather than designation, and a better career model than Tester → Lead → Manager Why AI-powered features raise the premium on judgement over tooling, and why a new certification should follow your career decision rather than replace itThere is something I have noticed repeatedly during my years of experience in software testing. People who start their careers around the same time, work on similar products and build comparable testing skills can end up in very different places. One becomes someone whose opinion is sought when an important product or de

The qTest On-Prem August release is here. Three things worth your attention. The modern qTest UX is now available on-prem The interface we've been rolling out is now available to on-prem customers. It's opt-in, so you can switch it on in qTest Manager, take a look, and switch back if your team isn't ready. Your existing workflows are unaffected. Worth switching on sooner rather than later. The new interface will become the default in a future release, and the transition tends to be smoother when teams have already had time with it. Vera approvals now extend to Requirements and Defects If you run validated processes, this closes a real gap. Vera's approval workflow previously covered Test Cases and Test Runs only, which meant formal sign-off on a requirement or a defect record had to happen in a third tool, usually Jira. Vera's workflow and state engine now extend to Requirements and Defects, with support for multiple approval events. The full approval chain runs inside qTest and Vera.