在上一篇文章中,我们讨论了如何构建一个运维助手Agent。今天,我想分享另一个实际项目:如何构建一个测试助手Agent。这个项目源于我们一个大型互联网公司的真实需求 - 提升测试效率,保障产品质量。
从测试痛点说起
记得和测试团队讨论时的场景：
小张：每次新功能上线都要写很多测试用例,很耗时
小李：是啊,而且回归测试也很繁琐
我：主要是哪些测试场景？
小张：接口测试、UI测试、性能测试这些
我：这些场景很适合用AI Agent来协助经过需求分析,我们确定了几个核心功能：
- 测试用例生成
- 自动化测试
- 缺陷分析
- 测试报告生成
技术方案设计
首先是整体架构：
from typing import List, Dict, Any, Optional
from enum import Enum
from pydantic import BaseModel
import asyncio
class TestTask(Enum):
CASE = "case"
AUTOMATE = "automate"
ANALYZE = "analyze"
REPORT = "report"
class TestContext(BaseModel):
task_type: TestTask
project_info: Dict[str, Any]
code_changes: Optional[Dict[str, Any]]
test_history: Optional[List[Dict[str, Any]]]
class TestAssistant:
def __init__(
self,
config: Dict[str, Any]
):
# 1. 初始化测试模型
self.test_model = TestLLM(
model="gpt-4",
temperature=0.2,
context_length=8000
)
# 2. 初始化工具集
self.tools = {
"generator": CaseGenerator(),
"automator": TestAutomator(),
"analyzer": DefectAnalyzer(),
"reporter": ReportGenerator()
}
# 3. 初始化知识库
self.knowledge_base = VectorStore(
embeddings=TestEmbeddings(),
collection="test_knowledge"
)
async def process_task(
self,
context: TestContext
) -> Dict[str, Any]:
# 1. 分析任务
task_info = await self._analyze_task(
context
)
# 2. 准备数据
data = await self._prepare_data(
context,
task_info
)
# 3. 生成方案
plan = await self._generate_plan(
task_info,
data
)
# 4. 执行任务
result = await self._execute_task(
plan,
context
)
return result
async def _analyze_task(
self,
context: TestContext
) -> Dict[str, Any]:
# 1. 识别任务类型
task_type = await self._identify_task_type(
context.task_type
)
# 2. 评估优先级
priority = await self._evaluate_priority(
context
)
# 3. 确定策略
strategy = await self._determine_strategy(
task_type,
priority
)
return {
"type": task_type,
"priority": priority,
"strategy": strategy
}用例生成功能
首先实现测试用例生成功能：
class CaseGenerator:
def __init__(
self,
model: TestLLM
):
self.model = model
async def generate_cases(
self,
context: TestContext
) -> Dict[str, Any]:
# 1. 分析需求
requirements = await self._analyze_requirements(
context
)
# 2. 设计用例
cases = await self._design_cases(
requirements
)
# 3. 优化用例
optimized = await self._optimize_cases(
cases,
context
)
return optimized
async def _analyze_requirements(
self,
context: TestContext
) -> Dict[str, Any]:
# 1. 提取功能点
features = await self._extract_features(
context.project_info
)
# 2. 识别测试点
test_points = await self._identify_test_points(
features
)
# 3. 确定测试策略
strategy = await self._determine_test_strategy(
test_points
)
return {
"features": features,
"test_points": test_points,
"strategy": strategy
}
async def _design_cases(
self,
requirements: Dict[str, Any]
) -> List[Dict[str, Any]]:
cases = []
for point in requirements["test_points"]:
# 1. 设计测试场景
scenario = await self._design_scenario(
point
)
# 2. 生成测试步骤
steps = await self._generate_steps(
scenario
)
# 3. 添加验证点
verifications = await self._add_verifications(
steps,
point
)
cases.append({
"scenario": scenario,
"steps": steps,
"verifications": verifications
})
return cases自动化测试功能
接下来是自动化测试功能：
class TestAutomator:
def __init__(
self,
model: TestLLM
):
self.model = model
async def automate_tests(
self,
context: TestContext,
cases: List[Dict[str, Any]]
) -> Dict[str, Any]:
# 1. 准备环境
env = await self._prepare_environment(
context
)
# 2. 生成脚本
scripts = await self._generate_scripts(
cases,
env
)
# 3. 执行测试
results = await self._execute_tests(
scripts,
env
)
return results
async def _generate_scripts(
self,
cases: List[Dict[str, Any]],
env: Dict[str, Any]
) -> Dict[str, Any]:
scripts = {}
# 1. 生成测试框架
framework = await self._setup_framework(
env
)
# 2. 转换测试用例
for case in cases:
script = await self._convert_to_script(
case,
framework
)
scripts[case["id"]] = script
# 3. 添加通用功能
common = await self._add_common_functions(
framework
)
scripts["common"] = common
return scripts
async def _execute_tests(
self,
scripts: Dict[str, Any],
env: Dict[str, Any]
) -> Dict[str, Any]:
results = {}
# 1. 初始化执行器
executor = await self._init_executor(
env
)
# 2. 执行测试脚本
for case_id, script in scripts.items():
if case_id != "common":
result = await executor.run(
script,
scripts["common"]
)
results[case_id] = result
# 3. 收集结果
summary = await self._collect_results(
results
)
return {
"results": results,
"summary": summary
}缺陷分析功能
再来实现缺陷分析功能：
class DefectAnalyzer:
def __init__(
self,
model: TestLLM
):
self.model = model
async def analyze_defects(
self,
context: TestContext,
test_results: Dict[str, Any]
) -> Dict[str, Any]:
# 1. 收集数据
data = await self._collect_data(
context,
test_results
)
# 2. 分析缺陷
analysis = await self._analyze_defects(
data
)
# 3. 生成建议
suggestions = await self._generate_suggestions(
analysis
)
return suggestions
async def _analyze_defects(
self,
data: Dict[str, Any]
) -> Dict[str, Any]:
# 1. 分类缺陷
categories = await self._categorize_defects(
data["failures"]
)
# 2. 定位根因
root_causes = await self._identify_root_causes(
categories,
data
)
# 3. 评估影响
impact = await self._evaluate_impact(
root_causes,
data["project_info"]
)
return {
"categories": categories,
"root_causes": root_causes,
"impact": impact
}
async def _generate_suggestions(
self,
analysis: Dict[str, Any]
) -> Dict[str, Any]:
# 1. 修复建议
fixes = await self._suggest_fixes(
analysis["root_causes"]
)
# 2. 预防措施
preventions = await self._suggest_preventions(
analysis["categories"]
)
# 3. 改进建议
improvements = await self._suggest_improvements(
analysis
)
return {
"fixes": fixes,
"preventions": preventions,
"improvements": improvements
}报告生成功能
最后是测试报告生成功能：
class ReportGenerator:
def __init__(
self,
model: TestLLM
):
self.model = model
async def generate_report(
self,
context: TestContext,
test_results: Dict[str, Any],
analysis: Dict[str, Any]
) -> Dict[str, Any]:
# 1. 收集数据
data = await self._collect_data(
context,
test_results,
analysis
)
# 2. 生成报告
report = await self._generate_content(
data
)
# 3. 格式化报告
formatted = await self._format_report(
report
)
return formatted
async def _generate_content(
self,
data: Dict[str, Any]
) -> Dict[str, Any]:
# 1. 生成摘要
summary = await self._generate_summary(
data
)
# 2. 生成详情
details = await self._generate_details(
data
)
# 3. 生成建议
recommendations = await self._generate_recommendations(
data
)
return {
"summary": summary,
"details": details,
"recommendations": recommendations
}
async def _format_report(
self,
report: Dict[str, Any]
) -> Dict[str, Any]:
# 1. 添加图表
charts = await self._add_charts(
report
)
# 2. 格式化文档
document = await self._format_document(
report,
charts
)
# 3. 生成导出
exports = await self._generate_exports(
document
)
return {
"document": document,
"charts": charts,
"exports": exports
}实际效果
经过两个月的使用,这个测试助手Agent带来了显著的改善：
效率提升
- 用例生成效率提升70%
- 自动化覆盖率提高50%
- 报告生成时间减少80%
质量改善
- 测试更全面
- 缺陷发现更早
- 分析更深入
成本优化
- 人力投入减少
- 测试周期缩短
- 资源利用更优
实践心得
在开发这个测试助手Agent的过程中,我总结了几点经验：
场景驱动
- 关注重点场景
- 分步骤实现
- 持续优化
数据支撑
- 收集测试数据
- 分析改进点
- 量化效果
工具协同
- 工具要集成
- 流程要打通
- 反馈要及时
写在最后
一个好的测试助手Agent不仅要能生成测试用例,更要理解测试的本质,帮助测试团队提升测试效率和质量。它就像一个经验丰富的测试专家,在合适的时候给出恰当的建议。
在下一篇文章中,我会讲解如何开发一个翻译助手Agent。如果你对测试助手Agent的开发有什么想法,欢迎在评论区交流。
