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How ProductSquads Redefined QE: Challenges with Agile, DevOps, and AI-driven Testing
How ProductSquads Redefined QE: Challenges with Agile, DevOps, and AI-driven Testing
Playlist
- See Why Your Testing Framework Is Incorrect, Incomplete, or Inefficient β And Iβll Show You Why
- Transitioning from Manual Testing to Test Automation with Cypress
- Shift Happens: Driving Quality LeftβA Real-World Journey Across Five Teams
- Client Feedback & Quality Assurance in Web Design for Agencies
- Democratise Automation to Build Autonomy and Go-To-Market Faster
- Testing AWS applications locally and on CI with LocalStack
- Man Vs Machine: Finding (replicable) bugs post-release
- Shift-Left: Accelerating Quality Assurance in Agile Environments
- End-to-End Test Automation with Provar
- Fast-Tracking Project Delivery:Tips from a Recovering Perfectionist
- Crawl, Walk, Run...Fly - Take your build and test pipeline to the next level
- Revolutionizing Testing with Test Automation as a Service (TaaS)
- How Codemagic Mitigates Challenging Mobile App Testing Environments
- Fast and Furious: The Psychology of Web Performance
- Faster Feedback with Intelligent CD Pipelines
- Navigating the Future of Quality Engineering in 2024
- Using AI for Effective Test Generation
- Automated Test Execution Reporting
- How To Speed Up Our Work During Web Automation
- Building Products that Drive Better Results with Shortcut
- Shifting Accessibility Testing Left with LambdaTest and Evinced
- Testing Tomorrow: Unravelling the AI in QA Beyond Automation
- Flaky Tests from an Engineering Perspective
- Future Trends and Innovations in Gen AI for Quality Engineering
- Mastering User-Centric Mindset Unlocking Your Potential as a Tester
- From Brainwave to Inbox: Avo's Whimsical Adventure through AI-Powered Test Automation
- The Myth of βBest Practiceβ
- Impact and Potentials of GenAI to the IT Engineers
- Innovation Accelerated: The Intersection of AI and Quality Engineering
- Optimize Issue Tracking: Integrating SpiraTeam with LambdaTest
- The Power of Generative AI in Reducing Maintenance and Enhancing Speed
- Rethinking the Role of QA Profile
- Building Quality Software: AI-based testing approach with Jira and QMetry
- In-Depth with Playwright: A Modern Testing Framework
- Supercharge Your Data Quality Testing with AI/ML
- GenAI for Quality Transformation
- Creating Reliable and Scalable Test Automation Frameworks
- Upskilling Quality Engineers: A Success Story in SDET Transformation
- AI-Readiness: Are You Building the Future or Falling Behind
- Leading the Charge in Software Quality with Zero Bug Revolution
- Building a Test Automation Framework for TV Apps & Scaling at FX Digital
- Building High-Quality Teams: People, Process & Proof for QA Leadership
- Why Do We Have Bugs, and Why Do They Happen?
- GenAI in QA: Tiket's Approach to Evolving Quality Engineering
- Collaborative Remote Testing: How to Set Up & Run Effective Ensemble Sessions
- Simulating Real-World Scenarios: Balancing Precision and Practicality in Testing
- Building AI-Driven Test Automation Frameworks for QA Excellence : Challenges with Agile, DevOps, and AI-driven Testing
- Reinforcing Cybersecurity Beyond Functional Testing
- Building AI-Driven Test Automation Frameworks for QA Excellence
About The Video
Join us for an insightful episode featuring ππ‘ππ’π₯ππ¬π‘ ππ¨π‘ππ₯, Head of Quality Engineering at ProductSquads, as we explore how modern Quality Engineering (QE) is evolving to meet the demands of todayβs fast-paced software landscape.
From shorter release cycles to complex tech stacks, AI-driven test automation, and integrating QE into CI/CD, teams face new challenges every day.
Video Chapters
00:00 Welcome!
01:13 Guest Introduction
05:19 Identifying Pain Points in Quality Engineering
07:41 Implementing Agile and AI in Quality Engineering
17:50 Managing Shorter Release Cycles
20:05 Scaling Test Automation Efforts
24:03 Selecting AI/ML Tools for Testing
27:37 Addressing Biases in AI/ML Models
32:25 Ensuring Skills for Agile and AI Technologies
37:00 Conclusion
Key Topics Covered
Challenges in Traditional QE
Quality Engineering (QE) faced issues like lack of test coverage, excessive manual efforts, poor test data management, and ineffective collaboration, leading to production defects.
Adopting Agile, DevOps, and AI
ProductSquads improved QE by integrating Agile for early testing, DevOps for automation, and AI for predictive analytics, test case generation, and synthetic data creation.
Implementing Shift-Left Testing
By testing earlier in the development cycle, teams received faster feedback, improved test coverage, and enhanced collaboration between QA and developers.
CI/CD and Automated Testing
Automating testing through CI/CD pipelines ensured rapid execution, minimized manual work, and introduced AI-driven quality gates for reliability.
AI in Test Data and Automation
AI helped generate test cases, automate script writing, and create realistic test data, significantly reducing manual efforts and improving defect detection.
Scaling Test Automation
Standardizing automation frameworks, running parallel tests, and leveraging AI-assisted script generation enhanced efficiency and reduced execution time.
Handling Shorter Release Cycles
AI-optimized test selection and automated performance monitoring enabled faster releases while maintaining software quality.
Mitigating AI Biases
Continuous AI model training, human validation, and clear understanding of AI limitations ensured balanced and effective test automation.
Building a Skilled QE Team
Training programs, hands-on workshops, and collaboration across teams helped engineers stay updated on Agile, AI, and automation trends.
Measuring AI ROI in QE
Key metrics like reduced test creation time, increased defect detection, and faster test execution validated AIβs impact on QE efficiency.
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LambdaTest is an AI-Native test orchestration and execution platform that allows you to perform both manual and automated testing across 3000+ environments, making it a top choice among other cloud testing platforms.
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