QA Hub AI Management Forum: Shaping the Future of Testing

Introduction

Introduction

Welcome to QA Hub's AI Management Forum, where leading industry professionals delve into the intersection of AI and testing. This forum highlights how AI is revolutionizing quality assurance and impacting our approach to testing procedures.

Opening Remarks

Introduction by Gar

Hi, I’m Gar, a Testing Manager in Nal from Kazakhstan, and your moderator for today’s event. Today with me is Camila, a Software Testing Engineer and coordinator of the QA Community.

Introduction by Camila

Hi everyone, my name is Camila. I’m honored to be your moderator. We'll explore AI's role in enhancing quality assurance.

Key Topics Covered

• Future of testing
• Introducing AI technologies to test management
• Using generative AI in software testing
• Approaches in testing AI applications
• AI-first wave in quality engineering

Earning QA Hub Coins

Participants can earn QA Hub coins by sharing the forum on Instagram or LinkedIn. Attendees who share on both platforms will earn additional coins.

About QA Hub

QA Hub is a global community focused on quality assurance. More than 5,500 followers from over 30 countries benefit from our events, podcasts, and community support.

Conference Schedule

Adam Arbach

Presentation Overview

Adam Arbach, VP of EPAM, discusses the future of testing and how AI tools can be integrated into continuous testing models.

Key Points

1. Evolution from Waterfall to Agile
2. Importance of Continuous Testing
3. Role of AI in DevOps
4. Steps to Implement AI in Quality Assurance

Natalia and Alexi

Presentation Overview

Natalia and Alexi talk about testing rag applications and the unique challenges posed by AI-infused testing.

Key Points

1. Introduction to Rag
2. Difference Between Traditional and AI Testing
3. Challenges in AI Testing
4. Metrics and Tools for AI Testing

Marina

Presentation Overview

Marina discusses AI-Enhanced Test Management and provides practical strategies and tools to mitigate stress in dynamic environments.

Key Points

1. AI-enhanced methodologies
2. Case studies in high-stress scenarios
3. Tools and platforms for integrating AI

Sohel Boradia

Presentation Overview

Sohel Boradia delves into an AI-first quality engineering approach, discussing the adoption and integration of generative AI within all phases of the SDLC.

Key Points

1. Importance of adopting generative AI
2. Practical steps for implementation
3. Case studies and success rates

Igor Bitkovski

Presentation Overview

Igor discusses gen-assistance in testing, showcasing various tools and success stories from EPAM. He elaborates on practical strategies for adopting AI in testing.

Key Points

1. Adoption Waves of AI
2. Success Stories
3. Calculating ROI for Generative AI
4. Strategies for Continuous Learning

Keywords

• AI in Testing
• Quality Assurance
• Generative AI
• Continuous Testing
• DevOps
• Rag Applications
• AI-Enhanced Management
• AI-first Quality Engineering
• EPAM
• Test Automation

FAQ

What is QA Hub?

QA Hub is a global community dedicated to professionals in quality assurance, providing events, talks, and resources to enhance testing expertise.

How can I earn QA Hub coins?

Share your experiences from QA Hub forums on Instagram or LinkedIn. Follow us, and ensure your profile is public.

How is AI changing the future of testing?

AI is revolutionizing testing by enhancing continuous testing models, generating test cases, and integrating with CI/CD pipelines to produce faster, more reliable results.

What are some metrics used in AI testing?

Metrics for AI testing include answer relevance, accuracy, and fairness across demographics. Custom data metrics for rag applications also play a significant role.

What is generative AI?

Generative AI refers to algorithms that create new content, including text, code, and even simulations, which are transforming traditional testing methodologies.

How do I get started with AI in testing?

Begin by assessing your current SDLC to identify pain points, choose appropriate AI tools like project Alita, and gradually implement and refine AI capabilities within your testing workflows.

Can existing QA skills be translated into AI-driven models?

Yes, QA professionals can adopt new AI skills like prompt engineering and data pruning, enhancing their ability to contribute effectively in an AI-driven environment.