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One of the biggest hurdles when testing AI-powered features is the oracle problem—it’s hard to state what the “expected result” should be, especially for language outputs that vary from run to run. Formal guidance for testing AI systems even calls this out explicitly.The tip: whenever your product uses an LLM, force its response into a strict JSON Schema and assert on fields—just like any other API. OpenAI, for example, supports Structured Outputs that guarantee responses conform to a schema; Microsoft’s Semantic Kernel shows the same approach in app code. This turns fuzzy text into deterministic fields your tests can validate.Why this works (and what to watch for)From prose to contracts. A JSON Schema acts as a “contract” (types, enums, required fields). When the model adheres to your schema, your test can assert on booleans, enums, and numbers—not on paragraphs. Vendor‑agnostic pattern. Independent evaluations show multiple vendors can produce structured JSON at scale, though APIs di

MCP Certification course by Karthik K.K. COURSE INTROCourse description:This video series introduces QA professionals to the Model Context Protocol (MCP) — a powerful way to connect testing tools, data sources, and automation workflows with AI-driven efficiency. Across ten practical sessions, you’ll learn how MCP simplifies integration challenges, streamlines test execution, enhances reporting, and makes QA assets — from databases to UI tests — more accessible and maintainable. Whether you’re looking to modernize your existing frameworks or prepare for the future of AI-assisted testing, this course gives you the knowledge and hands-on skills to bring MCP into your QA practice.📁Structure of the course: Introduction Installing MCP Server UI Testing With Playwright MCP Server UI Testing VS Code CodeGen API Testing With MCP Server DB Testing With MCP Server File System MCP Server BDD Test Case Agent Test Management MCP Server Future State of MCP Server​​​​​✅CertificationComplete all chapt

Do not prepare for interviews.Prepare for the job. Prepare the value you will add. If you are still preparing for interviews in 2026, you are already behind.Here is why:AI can coach anyone on interview questions now. Online courses teach you tools in 30 days. Anyone can be trained to "answer well." But here is what twenty years in the testing industry has taught me: interviewers don't hire you for good answers. They hire for your proof of impact and potential.They want to see what you have actually delivered. What have you improved? What have you learned and applied? What value did you add?That's what stands out. That’s what hiring managers like me expect.The Smartest Preparation Starts Before You Need a JobMost testers apply for roles first. Then they try to fit themselves into the requirements.Actually, it’s backwards.Self-awareness:Before you apply for a position, do a personal SWOT analysis.Know your actual strengths - not the ones you think employers want to hear. Understand your

In the previous chapter, I introduced the 5W1H AI agent framework and explored What AI agents are, How they work in testing contexts, and Why they matter.Now, we will dive deeper into this framework to understand When to use them, Where they fit, and Who provides agentic capabilities.When to Use Which Level of Agent?Agents come in various levels.  Each level solves a different type of problem. Some follow fixed rules. Some help you think. Some run tasks on their own. You need to select the level that fits your workload, risk, and control needs. This section will break down what each level does and when you should use it.Level 1: Rule-Based Systems (Traditional Agents)These are traditional automation agents that operate on predefined rules and logic. You can also call them as “Pre-LLM era” agents or traditional agents. Built using: Programmable rules. Fixed conditions. If-else logic. Behavior:Executes tasks exactly as programmed No reasoning capabilities (as they are not built using LLM

🎓 Course Overview: Generative AI for TestersGenerative AI is revolutionizing software testing, transforming it from a futuristic concept into a present-day necessity. To equip testers with the skills needed to navigate this evolving landscape, Tricentis presents the Generative AI for Testers course. This interactive mini-course is designed to help testers understand, apply, and benefit from Generative AI in their testing processes.📁Structure of the course: Introduction to Generative AI for Testers Deep Dive Leveraging LLMs and Mastering Prompt Engineering Implementation Strategies and What Comes Next🧠 What You'll LearnBy the end of this course, you will be able to: Explain the fundamentals of Generative AI, how it differs from traditional AI, and its role in software testing. Identify and apply AI-powered testing techniques, including prompt engineering, in-context learning, and model selection. Evaluate the advantages, limitations, and biases of Generative AI models to make inf

Testing has always lived under pressure. New features arrive before you finish reading the last requirements. Stakeholders want speed, yet expect zero mistakes. Team members leave, and new members join. New tools and technological breakthroughs emerge continuously. Through it all, testers carry the responsibility of understanding the product, interrogating the system, generating data, designing scenarios, executing checks, analyzing failures, and communicating the much-awaited “truth” about the product status. That too quickly, clearly, and consistently.A lot of this work is repetitive. A lot of it requires intense attention. Testers want speed but cannot compromise on judgment. They want tools that extend their capability. However, most testing tools often end up creating another task to manage.AI agents in testing offer a shift. They automate the predictable and static problems. They accelerate the variable and dynamic problems. They can pivot to your context, remember patterns, leve

NeoLoad Web’s next-generation UI (2025.1+) introduces Augmented AnalysisWhich is a machine‑learning assistant that segments your run into color‑coded intervals and flags where performance holds, changes, or degrades based on RED metrics (Rate, Errors, Duration). It’s paired with a V4 API and an API‑first architecture so you can automate analysis, reporting, and governance at CI/CD speed.Why this matters nowIn a shift‑left world, performance testing isn’t a once‑per‑release event: engineers see multiple test results per hour as pipelines auto‑trigger runs across services and APIs. The challenge has moved from running tests to prioritizing analysis—finding the few minutes in a long timeline where things truly go wrong. Augmented Analysis attacks exactly that problem.What Augmented Analysis does (and doesn’t)From Run to Root Cause with Augmented AnalysisWhat it is:In NeoLoad Web 2025.1, Augmented Analysis automatically partitions a test run into intervals and labels them (e.g., stable und

Performance Testing & Distributed Load Testing CourseBy: Japneet Sachdeva Course description:This hands-on course gives you a complete, practical introduction to performance testing; from software installation and basic test design to advanced, production-grade distributed load testing using Docker.You’ll learn how to create realistic user scenarios, test APIs end to end, validate responses, work with data-driven inputs, and run tests efficiently from the command line with detailed HTML reports.By the end, you’ll know how to design, execute, and analyze large-scale load tests and have the skills to apply JMeter confidently in real QA, DevOps, and engineering environments.📁Structure of the course:  Download and Installation Understanding Performance vs Load vs Stress Testing JMeter GUI Overview Thread-Groups Overview in JMeter Samplers Overview in JMeter First Load Test Execution JMeter Timers JMeter Assertions HTTP Managers Overview in JMeter CLI Execution using

Ever wished your automated test results could instantly tell you which requirements they cover? This week’s tip explores how Tricentis qTest leverages AI to enhance automation integration with smart traceability and reporting. For QA teams juggling many tests, qTest’s AI-assisted features can automatically link test outcomes to requirements and deliver intelligent analytics on your test run data – all in one place. The result is a more connected, insightful view of your testing efforts, without the manual overhead.Why Use AI-Powered Traceability in qTest?Modern test management isn’t just about storing test cases – it’s about connecting the dots between user stories, tests, and results. Tricentis qTest is an AI-augmented test management tool designed for exactly this purpose. It provides unified visibility into your testing, with predictive insights and intelligent reporting that tie together requirements, test cases, and automation results. In practice, this means qTest can automatical

In the ever-evolving world of software testing, AI is opening up transformative possibilities for QA teams. Tricentis has leveraged its years of AI innovation (from Vision AI to self-healing tests) to introduce Agentic Test Automation – an AI-powered “co-worker” embedded in Tricentis Tosca that can autonomously generate and execute test cases from natural language descriptions. This advanced capability is poised to revolutionize how QA engineers and software testers design automated tests, addressing modern challenges of scale and complexity in software delivery.Modern Testing Challenges and the Need for AI AssistanceDevelopment cycles are accelerating and codebases are growing larger than ever. With the advent of generative coding tools, developers can produce code at unprecedented speed – but this leaves QA teams scrambling to test more functionality in less time. Moreover, AI-generated code can introduce unexpected issues and variations, demanding smarter testing strategies. Traditi