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Using AI to Enhance Testing

In recent years, advancements in Artificial Intelligence have begun to reshape the discipline of software testing. Rather than replacing testers, AI enhances and extends testing capabilities by automating repetitive tasks, improving failure analysis, and enabling deeper exploration of system behavior. This session presents a practical and realistic view of how AI can be applied today to improve testing outcomes – focusing on areas that deliver measurable value without overpromising full autonomy.

We will explore how AI-driven tools and machine learning techniques are transforming key testing activities such as test case design, defect prediction, impact analysis, and regression optimization. High-impact use cases will be examined, including AI-assisted test generation, intelligent test maintenance, ML-based flaky test detection, and agent-based exploratory testing. Popular tools such as Playwright, Applitools, Launchable, ReportPortal, and emerging autonomous testing frameworks will be referenced to illustrate current industry practices.

The session will also address challenges introduced by AI, including oversight requirements, risks of over-reliance, and the need for governance in AI-generated test artifacts.

The core message: AI does not replace testers – It empowers them to focus on higher-value analysis, faster learning cycles, and more resilient quality strategies.

Key Takeaways

1. Understand how AI enhances test design, execution, and defect analysis in real projects.
2. Discover current trends such as generative testing, autonomous testing agents, and AI-driven quality insights.
3. Learn how to manage the new testing risks and governance challenges introduced by AI systems.

Target Audience
– Software testers, QA engineers, and test automation specialists
– Test managers, quality leaders, and engineering leads
– Anyone interested in the future of intelligent, AI-assisted testing

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