BugBlitz-AI: An Intelligent QA Assistant
May 17, 2024 Β· Declared Dead Β· π 2024 IEEE 15th International Conference on Software Engineering and Service Science (ICSESS)
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Authors
Yi Yao, Jun Wang, Yabai Hu, Lifeng Wang, Yi Zhou, Jack Chen, Xuming Gai, Zhenming Wang, Wenjun Liu
arXiv ID
2406.04356
Category
cs.SE: Software Engineering
Cross-listed
cs.AI
Citations
3
Venue
2024 IEEE 15th International Conference on Software Engineering and Service Science (ICSESS)
Last Checked
4 months ago
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.
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