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Innovation Accelerated: The Intersection of AI and Quality Engineering
Innovation Accelerated: The Intersection of AI and Quality Engineering
Playlist
- 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
- 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
About The Video
Tune in to our newest episode of the πππ¦ππππππ¬π ππ±π©ππ«π’ππ§ππ (ππ) πππ«π’ππ¬ featuring ππ§ππ¨π§π² πππ©π₯ππ§, Head of QE at Accenture. In the episode, Antony shares insights into the transformative potential of AI and future trends in testing in Quality Assurance and Engineering as he describes its importance in enhancing speed, quality, and productivity.
Video Chapters
00:00 Welcome!
01:54 Leveraging AI to Predict and Prevent Quality Issues
04:26 Evolution of QA Engineer Role in the DevOps Pipeline
06:35 AI-Powered Testing in the Retail Space
11:37 Challenges in Adopting AI-Driven Quality Engineering
16:25 Measuring ROI of AI-Powered Testing
19:45 Emerging Trends in Quality Engineering
25:22 Conclusion
Key Topics Covered
Leveraging AI to Predict and Prevent Quality Issues Discussion on how AI can analyze historical data to predict potential quality issues before they occur. Use of AI to identify patterns in code or behavior that might lead to defects and simulate real-world scenarios to catch issues early.
Evolution of QA Engineer Role in the DevOps Pipeline QA engineers are increasingly involved earlier in the development lifecycle, collaborating with developers. Emphasis on integration testing, performance testing, and the continuous integration/continuous delivery (CI/CD) pipeline.
AI-Powered Testing in the Retail Space Example of how AI-powered testing can enhance automation and create test cases from user stories and acceptance criteria. Importance of improving customer interaction and user experience in retail through AI-driven quality engineering.
Challenges in Adopting AI-Driven Quality Engineering Challenges include data quality, ethical concerns, and navigating regulatory requirements. Advice on starting small with pilots and proof of concepts, investing in data quality and governance, and addressing ethical and regulatory concerns.
Measuring ROI of AI-Powered Testing Methods to measure ROI include time savings, cost reductions, defect detection, and increased test coverage. Emphasis on finding defects early and improving the user experience to ensure high customer satisfaction.
Emerging Trends in Quality Engineering Discussion on the potential resurgence of manual testing due to the use of low-code/no-code AI-powered tools. Integration of security practices in the DevSecOps pipeline and the importance of real-time feedback for continuous improvement.
Related Blogs & Hubs
Leveraging AI for Enhanced Quality Assurance and Test Accuracy in Software
Unleashing the Potential of AI in Testing β Future of Quality Assurance
LambdaTest
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