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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.04356 |
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| _version_ | 1866914828032933888 |
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| author | Yao, Yi Wang, Jun Hu, Yabai Wang, Lifeng Zhou, Yi Chen, Jack Gai, Xuming Wang, Zhenming Liu, Wenjun |
| author_facet | Yao, Yi Wang, Jun Hu, Yabai Wang, Lifeng Zhou, Yi Chen, Jack Gai, Xuming Wang, Zhenming Liu, Wenjun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_04356 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | BugBlitz-AI: An Intelligent QA Assistant Yao, Yi Wang, Jun Hu, Yabai Wang, Lifeng Zhou, Yi Chen, Jack Gai, Xuming Wang, Zhenming Liu, Wenjun Software Engineering Artificial Intelligence 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. |
| title | BugBlitz-AI: An Intelligent QA Assistant |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2406.04356 |