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🎓 Course Overview: Generative AI for TestersTL;DR/Key TakeawaysLearn Generative AI for software testing fundamentals, including what GenAI can and cannot do in QA workflows. Understand LLMs for testers in plain language, including how models use tokens, training data, and APIs to generate outputs. Apply a practical GenAI framework for QA: assistant, experimentation and transformation, coding and test automation, learning and summarization. Use GenAI for test case generation, test data generation, requirements review, risk identification, bug report writing, and test documentation. Improve test automation productivity with GenAI support for boilerplate code, refactoring, modularization, debugging, and rapid prototyping. Master prompt engineering for testers with proven prompt patterns that improve clarity, accuracy, and repeatability. Learn how to ground answers using RAG (Retrieval Augmented Generation) and when to use in-context learning vs fine-tuning. Choose the right model using L

How to use AI in your daily SDET workflow: a practical ATDD guideby Lewis Prescott
Your instructor Lewis Prescott:Senior QA Consultant at Hippo Digital, specialising in test leadership and integration testing. With 10+ years experience in QA working for the likes of ASOS, Cancer Research UK and IBM. Lewis is a mentor, international conference speaker and book author of Contract Testing in Action with Manning publications.Course descriptionLearn how to apply AI across the full Acceptance Test-Driven Development (ATDD) cycle — from analyzing requirements to reviewing automation code. This hands-on course walks SDETs and QA engineers through practical, AI-assisted techniques for every stage of the testing workflow, using real Jest examples to show the test-first cycle in action.What you'll learn:How to use AI to review pull requests, summarize code changes, and identify testing gaps How to assess testability and map test coverage against a testing model The fundamentals of ATDD — what it

Free Certification Course by Manish VermaTL;DR/ Key Takeaways:Learn Agile testing fundamentals aligned with the Agile Manifesto, and how testing works differently in Agile compared to traditional lifecycles. Work effectively in a whole team approach, collaborating with developers and business stakeholders as part of a cross functional Agile team. Understand key Agile approaches including Scrum, Kanban, and XP, and what they mean for planning, delivery, and quality. Build skills for collaborative user story creation, including writing testable user stories and defining clear acceptance criteria. Learn Agile planning essentials such as release planning vs iteration planning, and how testers add value in both. Use continuous integration to get early feedback, reduce integration risk, and support fast regression cycles. Apply core Agile testing methods including TDD, ATDD, and BDD, and understand when each is useful in an iteration. Use the test pyramid and testing quadrants to balance tes

IntroductionOver the last couple of years, I’ve found myself in a situation I never quite anticipated.Test cases are no longer always written by testers.They are increasingly being generated by AI.What started as experiments inside delivery teams quickly became standard practice. Teams began using AI to generate test scenarios from user stories, derive edge cases from requirements, suggest regression suites, and even recommend automation coverage.The promise was extremely attractive.Faster coverage. Reduced manual effort. Improved productivity. The ability to scale testing without scaling teams at the same rate.And honestly, the early results looked impressive.I’ve seen AI generate meaningful test cases in seconds that would normally take hours manually. I’ve seen it identify edge conditions that even experienced testers initially overlooked. In one of our enterprise programs, introducing AI into the test design phase improved our initial test coverage significantly within just a few s

Have you already started exploring how AI can transform software testing? We’re excited to announce the AI Testing Customer Innovation Challenge - an opportunity to share your ideas, showcase your work, and shape the future of testing with AI.What’s this challenge about?This challenge invites Tricentis EMEA customers to share how AI can transform testing. Whether through real implementations or bold new ideas.The challenge is simple: submit your idea or use case describing how AI is (or could be) applied in testing — and what impact it can deliver.We welcome both:Real-world implementations → solutions you’ve already built or are working on Forward-looking concepts →  innovative ideas that explore new possibilitiesWhat to include in your submission:A brief description of your idea The problem it addresses How AI is (or could be) used Is it implemented, in progress of a concept for now? The expected or achieved impact  How to participate:Submit your idea (you can submit more than one!) u

Tricentis Data Integrity (DI) is now available as a cloud-native solution — bringing continuous, automated data validation directly to every team. This release simplifies how organizations test, reconcile, and ensure accuracy across their data sources, eliminating manual bottlenecks and enabling trustworthy data at the speed of modern delivery. With DI’s low-code, cloud-based approach, teams can validate data across their end-to-end IT landscape and complex business processes — ensuring that every decision, report, and AI initiative is built on accurate, reliable, decision‑grade data. With this launch, customers gain:  Continuous data validation across hybrid and cloud landscapes, ensuring data reflects true business values at all times.  Low‑code test authoring that enables both technical and business users to define and run comprehensive data tests.  Automated anomaly detection that identifies data issues early and validates against business rules —before they spread across connect

How to integrate AI in QA organizationsby Laveena Ramchandani Your instructor Laveena RamchandaniSenior Test Manager at a major low-budget airline, specialising in quality leadership within data science and complex technologies. With 13+ years of experience in QA, Laveena is an international conference speaker, keynote presenter, podcast guest, and author of professional courses on quality engineering.Course description This free course teaches quality engineering leaders how to integrate AI into their QA processes (and how Tricentis products can support that journey). Across 7 chapters, you'll explore real-world case studies on how to do it right.What you will learn:How AI changes testing decisions, accountability, and quality ownership in QA teams Where AI adds value — and where it doesn't — across the QA life cycle How to introduce AI without weakening tester skills How to evolve from manual workflows to AI-assisted and fully agentic QA pipelines How to build lightweight AI governan

About Arun Vishwanathan:I work as a Senior Software Development Engineer in Test at Apple, Inc., in the Machine Learning organization. I create testing automation tools and frameworks for use within the team and across other teams, with a focus on efficiency and improving productivity. These tools help qualify the product stack as a whole and enrich the customer experience. As part of my role, I also present product health statistics to senior company executivesYou will learn:How Big Tech ships products fast without breaking quality How to assess and prioritize risk when product cycles move faster than test coverage can keep up Why testing ML systems requires a different mindset than traditional software testing How to make the impact of software testers visible How developers and testers can split ownership to ship fasterApple ships software to over two billion active devices. Every iOS release, every macOS update, every new Siri feature lands simultaneously on hundreds of millions of

Certification course by Karthik K.K. TL;DR/ Key Takeaway:Learn core AI concepts for QA, including AI, machine learning, and LLMs, so you can apply them confidently in testing workflows.  Master prompt engineering and context engineering to get higher quality, more reliable test outputs from AI tools. Use AI assisted testing to accelerate manual test design by extracting key details from requirements, specs, and meeting artifacts. Understand vibe testing and how AI powered QA engineers orchestrate test design and execution using modern AI tools. Get practical with popular AI tools used in testing and coding workflows, including IDE copilots and CLI assistants. Learn how to generate and improve automation faster, including creating tests, fixing flaky code, and understanding unfamiliar codebases. Use MCP servers with real browsers to generate realistic tests, not hallucinated scripts, across UI, API, database, and BDD workflows. Understand AI agents as the “hands and legs” of LLMs that c

 🎉 Celebrating Our User of the Month for May 2026: ​@dharmendratak  🎉We’re excited to recognize ​@dharmendratak as our User of the Month for May 2026! 🏆🎁 In Recognition of His Contributions, ​@dharmendratak  Will Receive:A curated gift box filled with exclusive goodies A special User of the Month badge for his profile 500 reward points credited to his account A personalized certificate celebrating his achievement Featured on the ShiftSync Homepage🌟 What Makes ​@dharmendratak ExceptionalMeaningful contributions: Consistently shares insightful, practical content that helps others solve problems and learn. Supportive presence: Welcoming and helpful—creates a positive, collaborative atmosphere. Strong technical knowledge: Explains complex topics clearly and provides useful guidance. Forward-thinking: Brings fresh ideas and innovative perspectives that inspire progress.​@dharmendratak: We’ve contacted you by email. Please check your inbox (and spam folder) to arrange prize delivery.Tha

Author: Ujjwal Kumar Singh, Senior QA Engineer · 8+ years in enterprise QA  · beinghumantester.github.ioKey takeawaysAI will not replace testers — but it will make undocumented tester expertise visible and urgent to address There are two types of invisible testing work: genuine professional expertise, and organizational debt carried by people AI test generation tools cannot perform causal reasoning about system failures — this requires human mental models built over years The adaptive capacity testers exercise daily is the most undervalued competency in software engineering Organizations must document risk models and acceptance criteria before AI integration, not after Testers who make their judgment explicit become more valuable as AI capabilities increaseFor years, software teams believed their testing processes were robust. In reality, many were functioning only because testers were quietly compensating for everything the process failed to capture.When a requirement arrived without

At Transform 2026, we’re celebrating the visionaries — pushing boundaries, redefining quality, and accelerating enterprise transformation. The Tricentis Customer Innovation Awards are back to recognize the individuals and teams driving real impact with Tricentis solutions.We’ll be honoring customer champions across three distinct categories:AI TrailblazerRecognizing visionary customers (individuals or teams) who are pioneering innovative applications of AI to drive meaningful impact for software quality. This award celebrates those pushing the boundaries of AI to create transformative solutions and set new standards in quality engineering.Enterprise Transformation LeaderSpotlighting customers (individuals or teams) who have driven successful business transformation by implementing or optimizing core systems and applications to improve operational efficiency and achieve business objectives — including platforms such as SAP and Oracle, as well as other mission-critical systems in regulat

TL;DR:Test management reports fall into two categories: informative (showing current status) and analytic (revealing trends and root causes). To make confident release decisions, focus on coverage heat maps to spot quality gaps, track test execution progress by release and priority, analyze defect patterns by root cause and environment, and establish consistent project structures so reports can scale across your enterprise. The goal isn't perfect data—it's actionable insight when you need to decide whether to ship or hold.The Two Types of Reports That MatterWhen you're staring at a dashboard trying to decide if your release is ready, you need to know what kind of question you're asking. Are you checking current status, or are you diagnosing a problem?Informative reports give you the overview. They show raw data: how many test runs passed, how many defects are open, what your execution progress looks like by day. These reports require minimal configuration. Filter by release or test cyc