General Discussion

Hello ShiftSync community!Welcome back to our biweekly AI Tip of the Week series, where we explore practical ways to integrate AI into testing workflows. In Agile environments, code changes roll in quickly – and rerunning every test case after each change can be overkill. This week’s tip is about using AI to prioritize test cases based on risk and recent changes. In short, we’ll leverage AI-driven analysis to automatically figure out which tests are most valuable to run when code changes, so you catch critical bugs faster with less effort.Illustration: AI-driven test prioritization focuses your regression suite on the riskiest, most impacted areas after each code update. Instead of running an entire test suite blindly, AI algorithms analyze what changed (and past failures) to select the high-impact tests that should be run first. This targeted approach saves time and finds defects sooner.How AI Helps Prioritize Test CasesModern AI tools can crunch through code changes, past test result

Hi everyone,Thank you to all who joined my session “AI Took My Job, So I Took Its Seat at the Table.” It was an absolute pleasure sharing my journey of how a single question from a CTO — “Why do we need human testers when GPT can generate test cases in seconds?” — led me to rethink the role of testers in the age of AI.If you couldn’t make it to the webinar live, the recording is now available hereIn the session, we explored: How AI can accelerate certain testing tasks Where it still falls short without human judgment Why the true value of a tester lies in asking the questions AI cannot imagine Now, I’d love to keep the conversation going. This thread is your chance to: Share your own experiences with AI in testing Challenge my perspective Ask me any follow-up questions you didn’t get to ask during the webinar Here’s the recording of the Q&A section that happened at the end of the webinar:  Feel free to drop your questions in the comments below. I’ll be here to respond and e

Performance can't be an afterthought in today’s CI/CD-driven world. Teams shifting perf tests left report ~30% testing time savings.Performance testing is evolving fast. What used to be a late-stage gatekeeping step is now becoming a continuous, integrated process. As teams adopt modern architectures like microservices and shift toward CI/CD pipelines, performance issues can surface earlier and more frequently.In response, new trends are shaping how we test under pressure.Vote and let us know why you chose that option in the comments down below – we’d love to hear your perspective.

Visual tests often flood QA teams with minor diffs (font antialiasing, dynamic ads, etc.). Instead of manual pixel checks, let AI-driven tools compare UI snapshots and flag only real layout bugs. For example, Tricentis Testim integrates with Applitools Eyes: it takes a baseline screenshot (first run) and then automatically compares new screenshots on each build using AI.Step-by-step method (Testim+Applitools example): Capture a Baseline: In Testim, enable the Visual Validation (Applitools) step. Run the test once to store a “golden” screenshot in Applitools Eyes. Run & Compare: On each test run, Applitools Eyes grabs the current UI screenshot and uses Visual AI to compare it against the baseline. It highlights discrepancies that matter (layout shifts, missing elements, color changes) and ignores irrelevant noise. Tweak Sensitivity: Configure the match level or ignore zones. For example, set Applitools’ Match Level to “Content” or “Layout” (instead of strict pixel match) so dyna

QA professionals know that analyzing load test results can be like finding a needle in a haystack. Modern performance testing tools are now incorporating AI to make this easier. This week’s tip is about using Tricentis NeoLoad’s AI-powered analysis to quickly identify performance bottlenecks in your load testing workflow. NeoLoad’s latest releases include an “Augmented Analysis” engine that automatically highlights anomalies in test results – helping you focus on the areas that matter instead of wading through pages of metrics.AI-Powered Anomaly Detection in Performance TestingTricentis NeoLoad leverages machine learning to auto-analyze load test data and flag unusual patterns. It looks at key indicators – Rate, Errors, and Duration (RED) – to spot when the system’s behavior deviates from the norm. For example, a sudden spike in error rates or a slowdown in response times during a test run can be immediately caught by the AI. As the Tricentis team explains, NeoLoad’s AI algorithms will

Once feared as a job-stealer, AI is now being welcomed as a helpful co-pilot in QA, with 75% of organizations investing in it to speed up testing and boost quality. QA professionals across performance, load, functional, and exploratory testing are all curious (and maybe a bit excited) to see how AI might make their work easier.Vote and let us know why you chose that option in the comments down below – we’d love to hear your perspective.Poll Question:

Hello ShiftSync community!Welcome back to our biweekly AI Tip of the Week series, where we explore practical ways to integrate AI into your software testing workflows. Whether you're a seasoned QA professional or just starting to explore AI tools, these tips are designed to provide actionable insights to enhance your testing processes.Let's dive into this week's tip:Implement Self-Healing Test Automation to Reduce Maintenance OverheadIn agile development environments, frequent code changes can lead to brittle test scripts that require constant updates. This maintenance can consume valuable time and resources. Enter self-healing test automation—an AI-driven approach that adapts to changes in the application under test, reducing the need for manual intervention.What is Self-Healing Test Automation?Self-healing test automation leverages AI algorithms to detect changes in the application's UI or structure and automatically adjust the test scripts accordingly. This ensures that your test su

Hey everyone!We’re kicking off a new biweekly series here onShiftSync: AI Tip of the Week — posted every other Wednesday.Each edition will deliver a short, practical tip for applying AI in software testing — covering test automation, edge case generation, defect prediction, and more. Whether you’re just experimenting or already building AI into your QA workflows, this series is for you.Let’s dive into the first tip 👇🧠 AI Tip of the Week #1: Use GPT to Auto-Generate Edge Test CasesNeed to test tricky input fields like emails, names, or search boxes?Instead of guessing edge cases, let AI handle it.🛠️ Try this prompt in ChatGPT (or similar):“Give me edge case test inputs for a [type] input field. The field accepts [format or type], and is used for [purpose].”Example:"Give me edge case test inputs for an email field. The field accepts standard email formats, and is used for user registration."You’ll get inputs like: veryverylongemailaddress@domain.com user+alias@sub.domain.co.uk "qu

We are excited to introduce Tricentis Transform 2025 — our boldest, most ambitious conference yet. Taking place in London and Nashville, this global event is designed to explore how AI is revolutionizing the way we build, test, and deliver software.We are looking for engaging speakers to inspire, educate, and lead the industry forward. Whether you’re a seasoned expert or an aspiring presenter, this is your chance to share your insights with a global audience.Topics of interest include:How you are using Tricentis products to drive innovation and business success Best practices and strategies for overcoming software testing challenges Lessons learned from implementing AI and advanced automation in testing workflows Key takeaways from your testing transformation journeyBe part of history! This is more than a conference—it’s the next evolution of quality engineering events.Submit your speaker proposal by June 1, 2025.