BugBlitz-AI: An Intelligent QA Assistant
This addresses a domain-specific problem for QA teams by automating manual tasks, though it appears incremental as it builds on existing automated testing methods.
The paper tackles inefficiencies in post-execution phases of software testing, such as manual result analysis and bug reporting, by introducing BugBlitz-AI, an AI-powered validation toolkit that automates these processes to reduce time and improve product quality.
The evolution of software testing from manual to automated methods has significantly influenced quality assurance (QA) practices. However, challenges persist in post-execution phases, particularly in result analysis and reporting. Traditional post-execution validation phases require manual intervention for result analysis and report generation, leading to inefficiencies and potential development cycle delays. This paper introduces BugBlitz-AI, an AI-powered validation toolkit designed to enhance end-to-end test automation by automating result analysis and bug reporting processes. BugBlitz-AI leverages recent advancements in artificial intelligence to reduce the time-intensive tasks of manual result analysis and report generation, allowing QA teams to focus more on crucial aspects of product quality. By adopting BugBlitz-AI, organizations can advance automated testing practices and integrate AI into QA processes, ensuring higher product quality and faster time-to-market. The paper outlines BugBlitz-AI's architecture, discusses related work, details its quality enhancement strategies, and presents results demonstrating its effectiveness in real-world scenarios.