Spaces:
Running
Running
File size: 7,295 Bytes
d825c91 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 |
from fastapi import APIRouter, HTTPException, Depends
from typing import List, Dict, Any, Optional
from loguru import logger
from pydantic import BaseModel
import json
from services.test_service import test_service
from services.ai_service import ai_service
router = APIRouter()
class TestCase(BaseModel):
id: str
title: str
preconditions: List[str]
steps: List[str]
expected_results: List[str]
priority: str
type: str
requirement_id: str
class ExportRequest(BaseModel):
test_cases: List[TestCase]
target: str
project_id: str
section_id: Optional[str] = None
@router.post("/generate")
async def generate_test_cases(
requirements: List[Dict[str, Any]],
ai_provider: str = "openai",
model: str = "gpt-3.5-turbo"
) -> List[Dict[str, Any]]:
"""
Generate test cases from requirements.
Parameters:
- requirements: List of requirements
- ai_provider: AI provider to use
- model: Model to use
"""
try:
test_cases = await test_service.generate_test_cases(
requirements=requirements,
ai_service=ai_service
)
return test_cases
except Exception as e:
logger.error(f"Error generating test cases: {str(e)}")
raise HTTPException(status_code=500, detail=str(e))
@router.post("/export")
async def export_test_cases(request: ExportRequest) -> Dict[str, Any]:
"""
Export test cases to test management tool.
Parameters:
- request: Export request containing test cases and target information
"""
try:
if request.target == "testrail":
if not request.section_id:
raise HTTPException(
status_code=400,
detail="section_id is required for TestRail export"
)
results = await test_service.export_to_testrail(
test_cases=request.test_cases,
project_id=int(request.project_id),
section_id=int(request.section_id)
)
elif request.target == "jira":
results = await test_service.export_to_jira(
test_cases=request.test_cases,
project_key=request.project_id
)
elif request.target == "qtest":
results = await test_service.export_to_qtest(
test_cases=request.test_cases,
project_id=int(request.project_id)
)
else:
raise HTTPException(
status_code=400,
detail=f"Unsupported export target: {request.target}"
)
return {
"status": "success",
"results": results
}
except Exception as e:
logger.error(f"Error exporting test cases: {str(e)}")
raise HTTPException(status_code=500, detail=str(e))
@router.post("/validate")
async def validate_test_cases(
test_cases: List[TestCase],
requirements: List[Dict[str, Any]]
) -> Dict[str, Any]:
"""
Validate test cases against requirements.
Parameters:
- test_cases: List of test cases
- requirements: List of requirements
"""
try:
# Create requirement coverage matrix
coverage = {}
for req in requirements:
coverage[req["id"]] = {
"requirement": req,
"test_cases": [],
"covered": False
}
# Map test cases to requirements
for test_case in test_cases:
if test_case.requirement_id in coverage:
coverage[test_case.requirement_id]["test_cases"].append(test_case)
coverage[test_case.requirement_id]["covered"] = True
# Calculate coverage metrics
total_requirements = len(requirements)
covered_requirements = sum(1 for req in coverage.values() if req["covered"])
coverage_percentage = (covered_requirements / total_requirements) * 100
# Identify uncovered requirements
uncovered_requirements = [
req["requirement"]
for req in coverage.values()
if not req["covered"]
]
return {
"status": "success",
"coverage_percentage": coverage_percentage,
"total_requirements": total_requirements,
"covered_requirements": covered_requirements,
"uncovered_requirements": uncovered_requirements,
"coverage_matrix": coverage
}
except Exception as e:
logger.error(f"Error validating test cases: {str(e)}")
raise HTTPException(status_code=500, detail=str(e))
@router.post("/prioritize")
async def prioritize_test_cases(
test_cases: List[TestCase],
requirements: List[Dict[str, Any]]
) -> List[Dict[str, Any]]:
"""
Prioritize test cases based on requirements and risk.
Parameters:
- test_cases: List of test cases
- requirements: List of requirements
"""
try:
# Create risk assessment prompt
prompt = f"""
Analyze the following requirements and test cases to determine test case priority.
Consider:
1. Requirement priority
2. Business impact
3. Technical complexity
4. Historical defect patterns
Requirements:
{json.dumps(requirements, indent=2)}
Test Cases:
{json.dumps([tc.dict() for tc in test_cases], indent=2)}
For each test case, provide:
1. Priority score (1-5)
2. Risk level (High/Medium/Low)
3. Justification
"""
# Get AI assessment
assessment = await ai_service.generate_response(prompt=prompt)
# Parse and apply prioritization
prioritized_cases = []
for test_case in test_cases:
# Find assessment for this test case
case_assessment = _find_case_assessment(
assessment["response"],
test_case.id
)
prioritized_cases.append({
"test_case": test_case,
"priority_score": case_assessment["priority_score"],
"risk_level": case_assessment["risk_level"],
"justification": case_assessment["justification"]
})
# Sort by priority score
prioritized_cases.sort(
key=lambda x: x["priority_score"],
reverse=True
)
return prioritized_cases
except Exception as e:
logger.error(f"Error prioritizing test cases: {str(e)}")
raise HTTPException(status_code=500, detail=str(e))
def _find_case_assessment(assessment_text: str, case_id: str) -> Dict[str, Any]:
"""Extract assessment for a specific test case."""
# This is a simplified implementation
# In practice, you'd want more robust parsing
return {
"priority_score": 3,
"risk_level": "Medium",
"justification": "Default assessment"
} |