selfrelease
fad458b2a7
- UIUX 文档：填充 19 个缺口（多主体画像/健康度/AI+看板/增长域/洞察域/创始人端/OODA/助推/商密） - UIUX 文档：插入 6 个新章节（十四~十九），旧章节重编号为二十~三十一，更新目录和交叉引用 - 作业指导书 x5：导航改为 6 域分组，新增 Context Bar/工作模式/Insight Rail/决策线程/多工作区等 UI 概念 - 新建 docs/2-task-uiux.md：50 个代码落地开发任务，按 P0-P6 分优先级 + 8 Sprint 规划 - 后端/前端：大量新增模型、路由、组件（来自之前 Phase 开发）
220 lines
8.6 KiB
Python
220 lines
8.6 KiB
Python
"""T2.1 企业详情聚合接口测试 + T2.9/T3.11 健康度维度扩展测试。
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覆盖：
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- GET /companies/{id}/detail 聚合接口
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- 14 维度健康度评分计算
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- 趋势判断
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"""
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import pytest
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from fastapi.testclient import TestClient
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from app.services.health_calculator import (
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calculate_health_score,
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determine_trend,
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_calc_financial_score,
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_calc_operational_score,
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_calc_org_talent_score,
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_calc_product_tech_score,
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_calc_market_compete_score,
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_calc_governance_score,
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_calc_financing_score,
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_calc_synergy_score,
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_calc_ai_model_product_score,
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_calc_data_compliance_score,
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_calc_team_tech_score,
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_calc_customer_success_score,
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)
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class TestCompanyDetailEndpoint:
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"""T2.1 企业详情聚合接口测试。"""
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def test_get_company_detail_success(self, client: TestClient, auth_headers: dict, company_id: str):
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"""测试获取企业详情聚合数据。"""
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resp = client.get(f"/api/v1/companies/{company_id}/detail", headers=auth_headers)
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assert resp.status_code == 200
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data = resp.json()["data"]
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assert "company" in data
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assert data["company"]["id"] == company_id
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assert "recent_reports" in data
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assert "open_risks" in data
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assert "recent_weak_signals" in data
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assert "active_agreements" in data
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assert "recent_board_meetings" in data
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assert "financial_data_count" in data
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def test_get_company_detail_not_found(self, client: TestClient, auth_headers: dict):
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"""测试获取不存在的企业详情。"""
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resp = client.get("/api/v1/companies/nonexistent-id/detail", headers=auth_headers)
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assert resp.status_code == 404
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def test_get_company_detail_no_auth(self, client: TestClient, company_id: str):
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"""测试未认证请求。"""
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resp = client.get(f"/api/v1/companies/{company_id}/detail")
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assert resp.status_code == 401
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class TestHealthCalculator14Dimensions:
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"""T2.9 + T3.11 健康度 14 维度评分测试。"""
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def test_empty_data_returns_zeros(self):
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"""空数据应返回全 0。"""
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result = calculate_health_score({})
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assert result["total_score"] == 0.0
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assert result["financial_score"] == 0.0
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assert all(v == 0.0 for v in result.values())
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def test_none_data_returns_zeros(self):
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"""None 数据应返回全 0。"""
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result = calculate_health_score(None)
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assert result["total_score"] == 0.0
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def test_full_14_dimensions_present(self):
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"""完整数据应返回 14 个维度分数 + total_score。"""
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data = {
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"cash_balance": {"runway_months": 18},
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"revenue": {"yoy_change": "+30%"},
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"burn_rate": {"trend": "down"},
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"headcount": {"new_hires": 5, "departures": 1, "total": 50},
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"key_metrics": [
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{"name": "MAU", "change": "+15%"},
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{"name": "产品迭代次数", "change": "+10%"},
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{"name": "AI 推理成本", "change": "-5%"},
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],
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"governance": {"board_meeting_held": True, "compliance_issues": False},
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"financing": {"in_progress": True},
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"synergy": {"active_count": 3, "completed_count": 2},
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"ai_metrics": {"model_accuracy": 0.95, "inference_cost_trend": "down", "data_quality_score": 80},
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"data_compliance": {"issues_count": 0, "audit_passed": True},
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"team_tech": {"tech_lead_count": 3, "patent_count": 5},
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"customer_success": {"retention_rate": 0.92, "nps": 50},
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}
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result = calculate_health_score(data)
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expected_keys = [
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"total_score", "financial_score", "operational_score",
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"ai_commercial_score", "ai_cost_score",
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"org_talent_score", "product_tech_score", "market_compete_score",
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"governance_score", "financing_score",
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"synergy_score", "ai_model_product_score", "data_compliance_score",
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"team_tech_score", "customer_success_score",
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]
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assert all(k in result for k in expected_keys)
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assert 0 <= result["total_score"] <= 100
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def test_financial_score_high_runway(self):
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"""长跑道应得高分。"""
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data = {"cash_balance": {"runway_months": 15}, "revenue": {"yoy_change": "+20%"}, "burn_rate": {"trend": "down"}}
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score = _calc_financial_score(data)
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assert score >= 80
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def test_financial_score_low_runway(self):
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"""短跑道应得低分。"""
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data = {"cash_balance": {"runway_months": 2}, "revenue": {"yoy_change": "-10%"}, "burn_rate": {"trend": "up"}}
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score = _calc_financial_score(data)
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assert score < 40
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def test_org_talent_low_turnover(self):
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"""低流失率应得高分。"""
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data = {"headcount": {"new_hires": 3, "departures": 1, "total": 50}}
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score = _calc_org_talent_score(data)
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assert score >= 75
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def test_org_talent_high_turnover(self):
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"""高流失率应得低分。"""
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data = {"headcount": {"new_hires": 1, "departures": 12, "total": 50}}
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score = _calc_org_talent_score(data)
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assert score <= 50
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def test_product_tech_positive_growth(self):
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"""产品技术指标正增长应加分。"""
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data = {"key_metrics": [{"name": "产品迭代次数", "change": "+20%"}]}
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score = _calc_product_tech_score(data)
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assert score > 55
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def test_market_compete_positive(self):
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"""市场竞争指标正增长应加分。"""
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data = {"key_metrics": [{"name": "MAU", "change": "+25%"}]}
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score = _calc_market_compete_score(data)
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assert score > 55
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def test_governance_with_compliance_issues(self):
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"""有合规问题应减分。"""
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data = {"governance": {"board_meeting_held": True, "compliance_issues": True}}
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score = _calc_governance_score(data)
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assert score < 70
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def test_financing_long_runway(self):
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"""长跑道 + 融资进行中应得高分。"""
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data = {"cash_balance": {"runway_months": 20}, "financing": {"in_progress": True}}
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score = _calc_financing_score(data)
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assert score >= 80
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def test_synergy_active(self):
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"""有活跃协同应加分。"""
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data = {"synergy": {"active_count": 4, "completed_count": 2}}
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score = _calc_synergy_score(data)
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assert score > 55
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def test_ai_model_product_high_accuracy(self):
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"""AI 模型高准确率应加分。"""
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data = {"ai_metrics": {"model_accuracy": 0.95, "inference_cost_trend": "down", "data_quality_score": 85}}
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score = _calc_ai_model_product_score(data)
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assert score > 50
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def test_data_compliance_no_issues(self):
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"""无合规问题 + 审计通过应得高分。"""
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data = {"data_compliance": {"issues_count": 0, "audit_passed": True}}
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score = _calc_data_compliance_score(data)
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assert score >= 80
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def test_data_compliance_with_issues(self):
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"""有合规问题应减分。"""
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data = {"data_compliance": {"issues_count": 3, "audit_passed": False}}
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score = _calc_data_compliance_score(data)
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assert score < 50
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def test_team_tech_with_leads_and_patents(self):
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"""有技术负责人和专利应加分。"""
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data = {"team_tech": {"tech_lead_count": 3, "patent_count": 5}}
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score = _calc_team_tech_score(data)
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assert score > 55
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def test_customer_success_high_retention(self):
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"""高留存率应得高分。"""
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data = {"customer_success": {"retention_rate": 0.95, "nps": 60}}
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score = _calc_customer_success_score(data)
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assert score >= 75
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def test_customer_success_low_retention(self):
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"""低留存率应得低分。"""
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data = {"customer_success": {"retention_rate": 0.60, "nps": 10}}
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score = _calc_customer_success_score(data)
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assert score < 50
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def test_total_score_in_range(self):
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"""总分应在 0-100 范围内。"""
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data = {
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"cash_balance": {"runway_months": 6},
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"headcount": {"new_hires": 2, "departures": 1, "total": 30},
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"key_metrics": [{"name": "营收", "change": "+5%"}],
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}
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result = calculate_health_score(data)
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assert 0 <= result["total_score"] <= 100
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class TestDetermineTrend:
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"""趋势判断测试。"""
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def test_up_trend(self):
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assert determine_trend(80, 70) == "up"
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def test_down_trend(self):
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assert determine_trend(60, 70) == "down"
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def test_stable_trend(self):
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assert determine_trend(70, 72) == "stable"
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def test_no_previous(self):
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assert determine_trend(70, None) == "stable"
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