2025-10-30 –, Tutorial Room
Testing is evolving beyond static test scripts into intelligent, adaptive systems. This workshop introduces participants to AI-powered QA pipelines built with Python, leveraging multi-agent architectures, machine learning, and Retrieval-Augmented Generation (RAG). Through hands-on coding, we’ll explore how to build automation frameworks that detect bugs intelligently, adapt to application changes, and scale across CI/CD workflows. Participants will leave with practical skills to design next-generation QA solutions using tools like PyTest, Selenium, LangChain, and scikit-learn.
The modern software development landscape demands faster releases, higher reliability, and smarter testing frameworks. Traditional automated testing is powerful but limited—it often fails to adapt to evolving applications, resulting in brittle test suites and high maintenance costs. This workshop empowers participants to bridge that gap by introducing AI-powered QA systems built with Python.
We’ll begin with the fundamentals of AI in testing, discussing how multi-agent systems and Retrieval-Augmented Generation (RAG) can drive adaptive test case generation and bug detection. The workshop will then transition into practical, hands-on exercises where participants will build and extend their own Python-based intelligent QA pipeline.
Topics covered will include:
Foundations of AI-Powered Testing
Why traditional test automation fails in dynamic environments
Overview of intelligent testing and the role of multi-agent systems
Building Adaptive QA Pipelines
Using PyTest and Selenium to automate UI testing
Integrating LangChain and RAG for data-driven, adaptive test case generation
Intelligent Bug Detection
Applying machine learning models (scikit-learn, TensorFlow) to predict and detect failures
Anomaly detection and root cause analysis
Optimizing Test Coverage with Multi-Agent Systems
Designing autonomous agents that collaborate to expand test coverage
Continuous learning from application changes
CI/CD Integration
Running intelligent tests inside CI/CD pipelines (GitHub Actions, Jenkins)
Scaling QA automation for real-world engineering teams
By the end of this workshop, participants will:
Understand the principles of AI-driven QA systems
Gain practical coding experience in building adaptive testing frameworks with Python
Learn how to apply machine learning to improve bug detection and test coverage
Be equipped to integrate these systems into their existing DevOps workflows
This workshop is highly relevant to Developers, QA engineers, and DevOps professionals who want to reduce manual effort, build smarter test systems, and future-proof their QA processes
Hi, I’m Sneha Mavuri, a software engineer and QA specialist with 3 years of experience. I’m currently working at Swiggy, where I focus on ensuring product quality across web, mobile, and backend systems. I work hands-on with tools like Playwright, Appium, WebdriverIO, and Postman to catch bugs early and ensure a smooth experience for users.
Before Swiggy, I worked at CloudDefense.AI, Morgan Stanley, and Wingify, gaining experience across cloud security, backend development, and product testing. This journey has shaped my understanding of how to build products that are not just functional, but reliable and user-focused.
Outside of work, I’m a content creator with a community of over 22,000 followers on LinkedIn, where I share tech insights, career tips, and explain complex tools in simple ways. I enjoy helping others grow and stay curious about how we can use tech in meaningful ways.
Now, I’m channeling all my energy into building something of my own bringing together my skills in engineering, testing, and communication to create products that are smart, useful, and accessible.