- 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 探讨人工智慧工具和大型语言模型（LLMs）如何提升测试覆盖率、加速测试创建，并增强品质保证流程。
这项由讲师引导的培训（线上或线下）针对中级 QA 专业人士，旨在利用 AI 驱动的工具和实践自动化并提升测试策略。
培训结束后，参与者将能够：
- 使用 AI 工具和提示生成单元、整合和 UI 测试。
- 利用 LLMs 进行探索性测试、边界案例发现和回归分析。
- 使用 AI 辅助分类，对测试失败和异常进行分组和优先排序。
- 将基于 AI 的测试整合到 CI/CD 管道中，以提高发布信心。
课程形式
- 互动式讲座和讨论。
- 大量练习和实践。
- 在实时实验室环境中进行实际操作。
课程定制选项
- 如需为此课程定制培训，请联系我们进行安排。
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
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