QonfX

Retrieval-Augmented Generation (RAG) is transforming software testing by enhancing efficiency, accuracy, and scalability. It combines information retrieval with generative AI, enabling automated test artifact generation and improving knowledge retrieval for QA teams. One of its key benefits is automating test artifact generation by analyzing historical data, user stories, and documentation to create structured test cases that match new features. This speeds up test creation and reduces manual effort.

RAG also enhances knowledge retrieval by providing quick access to distributed information, historical data, and troubleshooting guidelines from sources like Confluence, GitHub, and Jira. QA engineers often need to search through large amounts of documentation and test reports to understand requirements, testing strategies, and known defects. With RAG, testers can retrieve relevant information more efficiently, helping new team members onboard faster and reducing the need for expert support.

This session will cover what RAG is and how it works, why it is relevant for software testing, and its key use cases. We will also discuss how to integrate RAG with existing CI/CD and QA workflows, structure test artifacts for effective retrieval, and address common challenges with practical solutions. Additionally, real-world case studies, lessons learned, and best practices will provide insights into leveraging RAG for software testing.

By the end of this session, you will understand how RAG accelerates testing processes by automating test case generation and knowledge retrieval, significantly reducing manual effort. It improves decision-making in QA by providing AI-driven insights, helping teams quickly identify impacted test cases and prioritize testing efforts. Moreover, RAG reduces costs and time-to-market by streamlining test design, execution, and defect analysis, allowing teams to focus on high-value tasks.