BugBlitz-AI: An Intelligent QA Assistant
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.
Authors
- Yi Zhou (ORCID: https://orcid.org/0000-0003-0565-9456)
- Zhenming Wang
- Xuming Gai
- Wenjun Liu (ORCID: https://orcid.org/0000-0001-6917-4111)
- Jun Wang (ORCID: https://orcid.org/0000-0002-0926-4761)
- Lifeng Wang (ORCID: https://orcid.org/0000-0001-5051-5991)
- Jack Chen
- Yi Yao (ORCID: https://orcid.org/0000-0003-4374-2955)
- Yabai Hu
Institutions
- Intel (United Kingdom) (GB)
Publication Details
- Published
- 2024-09-13
- DOI
- https://doi.org/10.1109/icsess62520.2024.10719045
- Citations
- 4
- Primary Topic
- AI-based Problem Solving and Planning
- Type
- article
- Field-Weighted Citation Impact
- 1.39