AI Agents Masterclass测试生成：自动化测试用例创建
项目地址: https://gitcode.com/GitHub_Trending/ai/ai-agents-masterclass 为什么需要自动化测试用例生成？
在AI Agent开发过程中，手动编写测试用例往往耗时且容易遗漏边缘情况。随着Agent功能的复杂性增加，测试覆盖率成为确保系统稳定性的关键因素。传统测试方法面临以下痛点：
- 重复劳动：相似的测试逻辑需要反复编写
- 覆盖不全：难以穷举所有可能的输入组合
- 维护困难：功能变更时需要同步更新大量测试用例
- 效率低下：手动测试无法跟上快速迭代的开发节奏
AI Agent测试框架设计原则
测试金字塔模型
核心测试组件
| 测试类型 | 测试目标 | 工具推荐 | 频率 |
|---|---|---|---|
| 单元测试 | 单个函数/方法 | pytest, unittest | 每次提交 |
| 集成测试 | 模块间交互 | pytest, Docker | 每日构建 |
| E2E测试 | 完整业务流程 | Playwright, Selenium | 发布前 |
自动化测试用例生成实战
基础测试框架搭建
首先创建测试目录结构：
tests/
├── unit/
│ ├── test_tools.py
│ ├── test_agents.py
│ └── conftest.py
├── integration/
│ ├── test_workflows.py
│ └── test_external_services.py
├── e2e/
│ └── test_full_workflow.py
└── fixtures/
├── mock_data.py
└── test_utils.py
单元测试示例：工具函数测试
import pytest
from unittest.mock import Mock, patch
from agents import create_asana_task, get_tools
class TestAsanaTools:
@pytest.fixture
def mock_asana_api(self):
"""模拟Asana API响应"""
with patch('agents.tasks_api_instance') as mock_api:
mock_response = {
"data": {
"gid": "12345",
"name": "Test Task",
"due_on": "2024-01-01"
}
}
mock_api.create_task.return_value = mock_response
yield mock_api
def test_create_asana_task_success(self, mock_asana_api):
"""测试成功创建Asana任务"""
result = create_asana_task("Test Task", "2024-01-01")
assert "12345" in result
mock_asana_api.create_task.assert_called_once()
def test_create_asana_task_with_today(self, mock_asana_api):
"""测试使用today作为due_on参数"""
with patch('agents.datetime') as mock_datetime:
mock_datetime.now.return_value.date.return_value = "2024-01-01"
result = create_asana_task("Test Task", "today")
assert "2024-01-01" in result
def test_get_tools_structure(self):
"""测试工具定义结构"""
tools = get_tools()
assert len(tools) == 1
assert tools[0]["type"] == "function"
assert "create_asana_task" in tools[0]["function"]["name"]
集成测试：Agent工作流测试
import json
from unittest.mock import patch, MagicMock
from agents import prompt_ai
class TestAgentWorkflow:
def test_agent_with_tool_calling(self):
"""测试Agent调用工具的工作流程"""
mock_messages = [
{"role": "system", "content": "You are a test assistant"},
{"role": "user", "content": "Create a task called 'Test Task' for today"}
]
# 模拟LLM响应，要求调用工具
mock_completion = MagicMock()
mock_completion.choices[0].message.tool_calls = [MagicMock()]
mock_completion.choices[0].message.tool_calls[0].function.name = "create_asana_task"
mock_completion.choices[0].message.tool_calls[0].function.arguments = json.dumps({
"task_name": "Test Task",
"due_on": "2024-01-01"
})
with patch('agents.client.chat.completions.create') as mock_create:
with patch('agents.create_asana_task') as mock_task:
# 第一次调用返回工具调用请求
mock_create.side_effect = [
mock_completion,
MagicMock() # 第二次调用返回最终响应
]
mock_task.return_value = json.dumps({"success": True})
result = prompt_ai(mock_messages)
assert mock_task.called
assert len(mock_messages) > 2 # 应该添加了工具调用和响应消息
端到端测试：完整业务流程
import asyncio
from playwright.async_api import async_playwright
class TestE2EAgent:
async def test_complete_agent_workflow(self):
"""端到端测试完整Agent业务流程"""
async with async_playwright() as p:
browser = await p.chromium.launch()
page = await browser.new_page()
# 启动Agent应用
await page.goto("http://localhost:8000")
# 输入任务创建指令
await page.fill("#chat-input", "Create a task for tomorrow called 'Important Meeting'")
await page.click("#send-button")
# 验证任务创建成功
await page.wait_for_selector(".task-created", timeout=5000)
task_text = await page.text_content(".task-created")
assert "Important Meeting" in task_text
assert "success" in task_text.lower()
await browser.close()
测试数据生成策略
使用Faker生成测试数据
from faker import Faker
import json
fake = Faker()
def generate_test_tasks(num_tasks=10):
"""生成测试用的任务数据"""
tasks = []
for _ in range(num_tasks):
task = {
"name": fake.sentence(nb_words=4),
"due_on": fake.date_this_year().isoformat(),
"description": fake.paragraph()
}
tasks.append(task)
return tasks
def generate_edge_case_tasks():
"""生成边界情况测试数据"""
edge_cases = [
{"name": "", "due_on": "2024-01-01"}, # 空任务名
{"name": "A" * 1000, "due_on": "2024-01-01"}, # 超长任务名
{"name": "Test Task", "due_on": "invalid-date"}, # 无效日期
{"name": "Test Task", "due_on": "9999-99-99"}, # 不可能日期
]
return edge_cases
测试覆盖率报告
配置pytest覆盖率检测：
# pytest.ini
[tool:pytest]
testpaths = tests
addopts = -v --cov=agents --cov-report=html --cov-report=xml
持续集成流水线
GitHub Actions配置
name: AI Agent CI
on:
push:
branches: [ main ]
pull_request:
branches: [ main ]
jobs:
test:
runs-on: ubuntu-latest
strategy:
matrix:
python-version: [3.9, 3.10, 3.11]
steps:
- uses: actions/checkout@v4
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install -r requirements.txt
pip install pytest pytest-cov
- name: Run unit tests
run: |
pytest tests/unit/ -v --cov=agents --cov-report=xml
- name: Run integration tests
run: |
pytest tests/integration/ -v
- name: Upload coverage reports
uses: codecov/codecov-action@v3
with:
file: ./coverage.xml
测试最佳实践
1. 模拟外部依赖
@pytest.fixture
def mock_openai_client():
"""模拟OpenAI客户端"""
with patch('agents.OpenAI') as mock_client:
mock_instance = mock_client.return_value
mock_instance.chat.completions.create.return_value = MagicMock()
yield mock_instance
2. 参数化测试
@pytest.mark.parametrize("task_name,due_on,expected", [
("Simple Task", "2024-01-01", True),
("", "2024-01-01", False), # 空任务名
("A" * 1000, "2024-01-01", False), # 超长任务名
])
def test_task_validation(task_name, due_on, expected):
"""参数化测试任务验证逻辑"""
result = validate_task(task_name, due_on)
assert result == expected
3. 异步测试处理
@pytest.mark.asyncio
async def test_async_agent_workflow():
"""测试异步Agent工作流"""
result = await async_prompt_ai(test_messages)
assert "success" in result.lower()
测试报告与分析
生成测试报告
# 生成HTML覆盖率报告
pytest --cov=agents --cov-report=html
# 生成JUnit格式报告
pytest --junitxml=test-results.xml
# 生成性能测试报告
pytest --benchmark-json=benchmark.json
测试指标监控
| 指标 | 目标值 | 监控频率 |
|---|---|---|
| 代码覆盖率 | >90% | 每次提交 |
| 测试通过率 | 100% | 每次运行 |
| 测试执行时间 | <5分钟 | 每日构建 |
| 缺陷发现率 | <1% | 每周评审 |
总结
自动化测试用例生成是AI Agent开发中不可或缺的一环。通过建立完善的测试体系，我们可以：
- 提高开发效率：减少手动编写测试用例的时间
- 增强代码质量：确保更高的测试覆盖率和代码稳定性
- 加速迭代速度：快速验证功能变更和重构
- 降低维护成本：自动化测试更容易维护和扩展
采用本文介绍的测试策略和工具链，你可以为AI Agents Masterclass项目构建强大的自动化测试基础设施，确保每个Agent都能可靠地运行在生产环境中。
记住：好的测试不是负担，而是加速开发的催化剂。投资测试基础设施的回报将在项目的整个生命周期中持续显现。
创作声明：本文部分内容由AI辅助生成（AIGC），仅供参考
