6th December 2023
05:00 PM - 07:00 PM MST
AI models are constantly improving, but they primarily rely on historical and publicly available data, which may not fully align with specific project needs, designs, or testing frameworks. They lack inherent knowledge of custom architectures, business logic, and domain-specific testing requirements. To provide relevant and tailored responses, AI must first learn about the project’s unique context.
A Knowledge Base helps by storing past test cases, API documentation, defect history, and system architecture. However, a static KB alone becomes outdated as projects evolve with UI changes, API updates, and new features. AI powered knowledge base i.e. Retrieval-Augmented Generation (RAG) solves this by dynamically fetching the latest test reports, logs, and design updates before AI generates responses.
By integrating real-time retrieval into AI-driven testing workflows, AI can continuously adapt, delivering accurate insights, automated script maintenance, and smarter defect analysis
Latha has close to 17 years of experience in software quality assurance, helping teams refine testing strategies and enhance software reliability. She has worked across top product-based and service-based industries, specializing in test automation for mobile, web, and enterprise applications. With a strong focus on efficiency and scalability, she has helped organizations identify gaps, establish robust quality processes, and implement smart automation to strengthen testing capabilities. Passionate about innovation, Latha works with teams to develop high-impact testing solutions that drive business success in an evolving technology landscape.
In this session, we will explore different software testing practices that can be applied in production, such as synthetic testing, covering its concepts, benefits, risks, and strategies. We will discuss how these approaches can enhance software quality by detecting failures in a real environment while also addressing challenges like user impact and security.
Additionally, we will present a real case study on applying automated tests to validate platform health in production, covering the challenges faced, strategies adopted, and the results achieved.
• Real-world case study on implementing automated tests for platform health validation
André de Oliveira is a QA Automation Lead specialized in software engineering and test automation. With over 15 years of experience, he has led technology projects across multiple platforms in the fintech sector. Throughout his career, he has managed QA and development teams, driving quality strategies, continuous testing initiatives, and DevOps adoption at companies like Devexperts and Itaú Unibanco. He holds CTFL, CBTS, and AWS Certified Developer certifications.
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