BugBlitz-AI: An Intelligent QA Assistant
Abstract
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.
Cite
@article{arxiv.2406.04356,
title = {BugBlitz-AI: An Intelligent QA Assistant},
author = {Yi Yao and Jun Wang and Yabai Hu and Lifeng Wang and Yi Zhou and Jack Chen and Xuming Gai and Zhenming Wang and Wenjun Liu},
journal= {arXiv preprint arXiv:2406.04356},
year = {2024}
}