Recently active topics

🎉 Today, we are celebrating our User of the Month for June 2026: ​@SudhaY We're delighted to announce ​@SudhaY  as our User of the Month for June 2026! 🏆Through her continued engagement, valuable insights, and willingness to support others, Sudha has made a lasting impact on the ShiftSync community. This recognition is our way of thanking her for the positive difference he makes every day.🎁 What ​@SudhaY Will ReceiveAs part of this recognition, he will receive:A curated gift box packed with exclusive goodies A special User of the Month profile badge 500 reward points added to her account A personalized certificate commemorating this achievement A featured spot on the ShiftSync Homepage🌟 Why ​@SudhaY Stands Out:💡 Valuable ContributionsSudha consistently shares practical insights and helpful content that empower community members to learn, troubleshoot, and succeed.🤝 Positive Community SupportShe is always approachable, supportive, and encouraging, helping create a welcoming enviro

TL;DR: Tricentis has launched a three-layer AI platform that lets QA teams generate test cases, automate testing, and orchestrate multi-tool workflows using natural language. The platform includes MCP servers (which connect AI tools like ChatGPT directly to Tricentis products), specialized AI agents trained on 15-20 years of testing expertise, and AI Workspace—a central hub for chaining together QA workflows across tools. Early adopters report 50-60% productivity gains.The Testing Bottleneck No One's Talking AboutDevelopers are moving faster than ever. GitHub's 2024 data shows developers using AI coding assistants like Copilot complete tasks 55% faster. That's a massive acceleration in code production. But here's the problem: QA hasn't kept pace.While developers have been leveraging AI assistants for over a year, quality engineering teams are still writing test cases manually, building automation scripts line by line, and stitching together workflows across disconnected tools. The resu

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 AMS 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 submissionA brief description of your idea The problem it addresses How AI is (or could be) used Is it implemented, in progress, or a concept for now? The expected or achieved impactHow to participateSubmit your idea (you can submit more than one!) using the

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.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 is, who

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

What You'll Learn:Requirement defects often cost more than coding defects because they affect architecture, development, testing, automation, and business validation. Many production incidents originate from questions that were never asked during requirement discussions. Accessibility, edge cases, exception handling, and system integrations are common sources of overlooked requirements. AI can help analyze documented requirements but cannot uncover knowledge that exists only in stakeholders' minds. How many defects did we find?This is the most common question I used to ask my team. My managers used to ask me in most of my projects.That was a measure of success.After 22 years in software testing, I have stopped measuring the success of testing by one simple metric: Today, I ask a different question.How many defects should never have reached testing in the first place?That means to say, how many defects did you prevent?That question has changed how I think about testing and user exp

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

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.Description This free tutorial 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 governance th

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

Free Upskill session 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 test lev

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