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所在平台: Udemy |
课程主页: https://www.udemy.com/course/genai-multi-agent-software-testing/
课程评论：没有评论
**课程名称：** 构建GenAI与多智能体系统工具以赋能软件测试 **课程概述：** 本课程是一门实践导向的课程，旨在教授学员如何利用生成式AI（GenAI）、AI智能体（AI Agents）和多智能体系统（Multi-Agent Systems）来构建实际的软件测试工具。无论您是QA工程师、SDET还是希望提升自动化技能的开发者，本课程都将为您提供实用的技术，将AI驱动的效率融入您的测试生命周期。 随着大型语言模型（LLMs，如ChatGPT、LLaMA、Gemini）以及LangChain和AutoGen等框架的快速发展，QA工程师的职能已不再局限于手动编写测试用例和检查日志。本课程将帮助您掌握构建自主测试智能体、自动化日志分析，甚至创建协作式多智能体测试系统的能力。 **学习目标：** * 理解GenAI、AI智能体和多智能体系统的核心概念。 * 学会使用Ollama在本地运行强大的开源LLMs（无需付费API）。 * 运用LangChain构建智能工具和智能体以实现QA自动化。 * 创建能够读取PDF、解析日志和生成测试用例的自定义工具。 * 学习使用向量存储（Vector Stores）结合嵌入（Embeddings）来存储和查询数据。 * 构建一个基于RAG（检索增强生成）的智能体，通过上下文检索来分析日志。 * 开发一个从产品需求生成测试用例的智能体。 * 利用Playwright结合智能体来模拟网页抓取和行为测试。 * 使用AutoGen和AutoGen Studio来编排多智能体协作。 * 构建能够读取需求并输出测试用例的全自动化智能体。 * 设计能够模拟真实QA工作流程、且人力投入最小化的多智能体QA系统。 **课程独特性：** 与其他侧重于聊天机器人或语言任务的AI课程不同，本课程深入研究了测试生命周期，并展示了如何为软件质量保证构建智能的、上下文感知强的智能体。您将不仅仅停留在理论层面，而是动手构建具有实际功能的工具，这些工具能够： * 读取您的需求 * 理解日志和测试结果 * 生成测试脚本和摘要 * 如同AI测试团队一样协同工作 所有这些都将通过开源工具、本地模型和实用的Python代码来实现。
Welcome to my course Build GenAI & Multi-Agent Systems Tools for Software TestingIn this hands-on course, you'll learn to harness the power of Generative AI, AI Agents, and Multi-Agent Systems to build real-world tools for software testing. Whether you're a QA engineer, SDET, or developer aiming to level up your automation skills, this course equips you with practical techniques to bring AI-driven efficiency into your testing lifecycle.Today, QA engineers are no longer limited to writing test cases and checking logs manually. With the rapid growth of LLMs (like ChatGPT, LLaMA, and Gemini) and frameworks like LangChain and AutoGen, you can now build autonomous test agents, automate log analysis, and even create collaborative multi-agent testing systems. This course gives you the tools, patterns, and hands-on skills to make that leap.By the end of this course, you will be able to:Understand the core concepts behind GenAI, AI Agents, and Multi-Agent SystemsRun powerful open-source LLMs locally using Ollama (no paid API needed)Use LangChain to build intelligent tools and agents for QA automationCreate custom tools that read PDFs, parse logs, and generate test casesStore and query data using vector stores with embeddingsBuild a RAG-powered agent that analyzes logs using context retrievalDevelop a Test Case Generator Agent from product requirementsUse Playwright with agents to simulate web scraping and behavior testingOrchestrate multi-agent collaboration using AutoGen and AutoGen StudioConstruct fully automated agents that read requirements and output test casesDesign multi-agent QA systems that mimic real QA workflows with minimal human inputWhy This Course is UniqueMost AI courses focus on chatbots or language tasks. This course goes deep into the testing lifecycle and shows you how to build intelligent, context-aware agents for software quality assurance. You'll move beyond theory and actually build working tools that:Read your requirementsUnderstand logs and test resultsGenerate test scripts and summariesWork together as a team of AI testersAll using open-source tools, local models, and practical Python code.
