- 具备软件测试、测试规划或QA自动化经验
- 熟悉JUnit、PyTest或Selenium等测试框架
- 对CI/CD管道和DevOps环境有基本了解
受众
- QA工程师
- 测试开发工程师（SDETs）
- 在敏捷或DevOps环境中工作的软件测试人员
AI-Augmented Test Generation and Quality Engineering 探討人工智慧工具和大型語言模型（LLMs）如何提升測試覆蓋率、加速測試創建，並增強品質保證流程。
這項由講師引導的培訓（線上或線下）針對中級 QA 專業人士，旨在利用 AI 驅動的工具和實踐自動化並提升測試策略。
培訓結束後，參與者將能夠：
- 使用 AI 工具和提示生成單元、整合和 UI 測試。
- 利用 LLMs 進行探索性測試、邊界案例發現和回歸分析。
- 使用 AI 輔助分類，對測試失敗和異常進行分組和優先排序。
- 將基於 AI 的測試整合到 CI/CD 管道中，以提高發布信心。
課程形式
- 互動式講座和討論。
- 大量練習和實踐。
- 在實時實驗室環境中進行實際操作。
課程定制選項
- 如需為此課程定制培訓，請聯繫我們進行安排。
Software Testing 中的 AI 简介
- AI 在测试和 QA 中的能力概述
- 现代测试工作流程中使用的 AI 工具类型
- AI 驱动的质量工程的益处与风险
用于测试案例生成的 LLMs
- 为生成单元和功能测试进行提示工程
- 创建参数化和数据驱动的测试模板
- 将用户故事和需求转换为测试脚本
探索性和边缘案例测试中的 AI
- 使用 AI 识别未测试的分支或条件
- 模拟罕见或异常的使用场景
- 基于风险的测试生成策略
自动化 UI 和回归测试
- 使用 Testim 或 mabl 等 AI 工具创建 UI 测试
- 通过自愈选择器维护稳定的 UI 测试
- 代码更改后基于 AI 的回归影响分析
故障分析与测试优化
- 使用 LLM 或 ML 模型对测试失败进行聚类
- 减少不稳定的测试运行和警报疲劳
- 基于历史洞察优先执行测试
CI/CD 管道集成
- 在 Jenkins、GitHub Actions 或 GitLab CI 中嵌入 AI 测试生成
- 在拉取请求期间验证测试质量
- 管道中的自动化回滚和智能测试门控
QA 中 AI 的未来趋势与负责任使用
- 评估 AI 生成测试的准确性和安全性
- Go治理和 AI 增强测试过程的审计跟踪
- AI-QA 平台和智能可观测性的趋势
总结与下一步
United Arab Emirates - AI-Augmented Test Generation and Quality Engineering
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