BugBlitz-AI: An Intelligent QA Assistant
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Navigating the growing field of research on AI for software testing -- the taxonomy for AI-augmented software testing and an ontology-driven literature survey
Test It Before You Trust It: Applying Software Testing for Trustworthy In-context Learning
Towards Trustworthy GUI Agents: A Survey
Assessing Data Augmentation-Induced Bias in Training and Testing of Machine Learning Models
Can Search-Based Testing with Pareto Optimization Effectively Cover Failure-Revealing Test Inputs?
Leveraging Large Language Models for Enhancing the Understandability of Generated Unit Tests
토론
이 논문과 연결된 포럼 글이 아직 없습니다.