fake_tapd_gen.py•15.3 kB
# fake_tapd_gen.py
"""
TAPD 数据生成器 - 用于生成模拟的 TAPD 需求和缺陷数据
功能：
- 生成模拟的需求（故事）数据，包含算法策略类和功能决策类
- 生成模拟的缺陷数据，包含简单缺陷和详细复现步骤的缺陷
- 支持自定义生成数据量和输出文件路径
- 完全匹配真实TAPD API返回的数据结构
"""
from faker import Faker
import random, uuid, json, pathlib, datetime
# 兼容导入：支持包内相对导入与脚本直接运行
try:
from .common_utils import get_file_manager # type: ignore
except Exception:
import os, sys
sys.path.append(os.path.dirname(os.path.dirname(__file__)))
from mcp_tools.common_utils import get_file_manager # type: ignore
fk = Faker("zh_CN")
# 生成长数字ID（匹配TAPD格式）
def generate_tapd_id():
return f"113785767800100{random.randint(1000, 9999)}"
# 生成TAPD格式的时间字符串
def generate_tapd_time():
dt = fk.date_time_between(start_date='-1y', end_date='now')
return dt.strftime('%Y-%m-%d %H:%M:%S')
# ——— 模板列表 ———
DOC_LINK_TPL = "https://docs.qq.com/sheet/DVkt0TGZaTXhWcVZC?tab={}"
IMG_URL_TPL = "/tfl/pictures/202507/tapd_37857678_{}_521.png"
VIDEO_URL = "https://samplelib.com/mp4/sample-5s.mp4"
def fake_doc_link():
return f"https://docs.qq.com/doc/DZmZnc0JubVpGc0{uuid.uuid4().hex[:2]}"
def fake_img_url():
return IMG_URL_TPL.format(random.randint(1000000, 9999999))
def fake_demand_A():
"""生成算法策略类需求"""
created_time = generate_tapd_time()
modified_time = generate_tapd_time()
story_id = generate_tapd_id()
base_story = {
"id": story_id,
"workitem_type_id": "1137857678001000007",
"name": f"【算法策略】{fk.word()}接入元宝需求",
"workspace_id": "37857678",
"creator": "TAPD",
"created": created_time,
"modified": modified_time,
"status": random.choice(["planning", "developing", "resolved", "status_2", "status_3"]),
"begin": fk.date_between(start_date='-6m', end_date='+1m').strftime('%Y-%m-%d'),
"due": fk.date_between(start_date='+1m', end_date='+6m').strftime('%Y-%m-%d'),
"priority": str(random.randint(1, 4)),
"iteration_id": random.choice(["0", "1137857678001000003", "1137857678001000004", "1137857678001000005", "1137857678001000006"]),
"completed": created_time if random.choice([True, False]) else None,
"category_id": random.choice(["-1", "1137857678001000010"]),
"path": f"{story_id}:",
"parent_id": "0",
"children_id": "|",
"ancestor_id": story_id,
"level": "0",
"effort": str(random.randint(1, 5)),
"effort_completed": "0",
"exceed": str(random.randint(-5, 5)),
"remain": str(random.randint(0, 5)),
"release_id": "0",
"templated_id": "1137857678001000010",
"created_from": "mindmap",
"label": random.choice(["", "阻塞", "有风险"]),
"progress": str(random.randint(0, 100)),
"is_archived": "0",
"secret_root_id": "0",
"progress_manual": "0",
"custom_plan_field_1": "0",
"custom_plan_field_2": "0",
"custom_plan_field_3": "0",
"custom_plan_field_4": "0",
"custom_plan_field_5": "0",
"custom_plan_field_6": "0",
"custom_plan_field_7": "0",
"custom_plan_field_8": "0",
"custom_plan_field_9": "0",
"custom_plan_field_10": "0",
"priority_label": random.choice(["High", "Middle", "Low", "Nice To Have"])
}
# 只有约12.5%的需求有description字段（根据真实数据：2/16）
if random.random() < 0.125:
description = f"""<p>测试需求腾讯文档链接：</p><p>【腾讯文档】{fk.word()}算法策略文档</p><p><a href="{fake_doc_link()}" target="_blank" rel="noopener">{fake_doc_link()}</a></p><p><br /></p><p>需求示意图：</p><p class="tox-clear-float"><img src="{fake_img_url()}" style="width: 80%;" /></p>"""
base_story["description"] = description
# 约43.75%的需求有owner字段（根据真实数据：7/16）
if random.random() < 0.4375:
base_story["owner"] = f"{fk.name()};"
return base_story
def fake_demand_B():
"""生成功能决策类需求"""
created_time = generate_tapd_time()
modified_time = generate_tapd_time()
story_id = generate_tapd_id()
base_story = {
"id": story_id,
"workitem_type_id": "1137857678001000007",
"name": f"{fk.word()} 根据{fk.word()}结果决定是否请求读书接口",
"workspace_id": "37857678",
"creator": "TAPD",
"created": created_time,
"modified": modified_time,
"status": random.choice(["planning", "developing", "resolved", "status_2", "status_3"]),
"begin": fk.date_between(start_date='-6m', end_date='+1m').strftime('%Y-%m-%d'),
"due": fk.date_between(start_date='+1m', end_date='+6m').strftime('%Y-%m-%d'),
"priority": str(random.randint(1, 4)),
"iteration_id": random.choice(["0", "1137857678001000003", "1137857678001000004", "1137857678001000005", "1137857678001000006"]),
"completed": created_time if random.choice([True, False]) else None,
"category_id": random.choice(["-1", "1137857678001000010"]),
"path": f"{story_id}:",
"parent_id": "0",
"children_id": "|",
"ancestor_id": story_id,
"level": "0",
"effort": str(random.randint(1, 5)),
"effort_completed": "0",
"exceed": str(random.randint(-5, 5)),
"remain": str(random.randint(0, 5)),
"release_id": "0",
"templated_id": "1137857678001000010",
"created_from": "mindmap",
"label": random.choice(["", "阻塞", "有风险"]),
"progress": str(random.randint(0, 100)),
"is_archived": "0",
"secret_root_id": "0",
"progress_manual": "0",
"custom_plan_field_1": "0",
"custom_plan_field_2": "0",
"custom_plan_field_3": "0",
"custom_plan_field_4": "0",
"custom_plan_field_5": "0",
"custom_plan_field_6": "0",
"custom_plan_field_7": "0",
"custom_plan_field_8": "0",
"custom_plan_field_9": "0",
"custom_plan_field_10": "0",
"priority_label": random.choice(["High", "Middle", "Low", "Nice To Have"])
}
# 只有约12.5%的需求有description字段（根据真实数据：2/16）
if random.random() < 0.125:
desc = f"""<p>## 背景</p><p>{fk.paragraph(nb_sentences=3)}</p><p><br /></p><p>## 方案</p><p>{fk.paragraph(nb_sentences=4)}</p><p><br /></p><p>方案文档链接：</p><p><a href="{fake_doc_link()}" target="_blank" rel="noopener">{fake_doc_link()}</a></p><p><br /></p><p>方案示意图：</p><p class="tox-clear-float"><img src="{fake_img_url()}" style="width: 80%;" /></p>"""
base_story["description"] = desc
# 约43.75%的需求有owner字段（根据真实数据：7/16）
if random.random() < 0.4375:
base_story["owner"] = f"{fk.name()};"
return base_story
def fake_bug_A():
"""生成简单缺陷"""
created_time = generate_tapd_time()
modified_time = generate_tapd_time()
bug_id = generate_tapd_id()
# 为缺陷添加description字段，包含HTML格式的链接和图片
description = f"""<p>测试腾讯文档链接：</p><p>【腾讯文档】{fk.word()}缺陷报告文档</p><p><a href="{fake_doc_link()}" target="_blank" rel="noopener">{fake_doc_link()}</a></p><p><br /></p><p>测试图片：</p><p class="tox-clear-float"><img src="{fake_img_url()}" style="width: 80%;" /></p>"""
return {
"id": bug_id,
"title": f"{fk.word()}按钮点击无反应",
"description": description,
"priority": random.choice(["medium", "high", "low"]),
"severity": random.choice(["normal", "serious", "slight"]),
"status": random.choice(["new", "closed", "resolved"]),
"reporter": "TAPD",
"created": created_time,
"modified": modified_time,
"lastmodify": fk.name(),
"version_report": f"版本{random.randint(1, 3)}",
"iteration_id": random.choice(["1137857678001000003", "1137857678001000004", "1137857678001000005"]),
"regression_number": str(random.randint(0, 3)),
"release_id": "0",
"template_id": random.choice(["0", "1137857678001000013"]),
"size": str(random.randint(1, 3)),
"effort": str(random.randint(1, 3)),
"effort_completed": "0",
"exceed": str(random.randint(-3, 3)),
"remain": str(random.randint(0, 3)),
"secret_root_id": "0",
"custom_plan_field_1": "0",
"custom_plan_field_2": "0",
"custom_plan_field_3": "0",
"custom_plan_field_4": "0",
"custom_plan_field_5": "0",
"custom_plan_field_6": "0",
"custom_plan_field_7": "0",
"custom_plan_field_8": "0",
"custom_plan_field_9": "0",
"custom_plan_field_10": "0",
"priority_label": random.choice(["medium", "high", "low"]),
"workspace_id": "37857678"
}
def fake_bug_B():
"""生成详细复现步骤的缺陷"""
created_time = generate_tapd_time()
modified_time = generate_tapd_time()
bug_id = generate_tapd_id()
# 详细的复现描述，包含HTML格式的文档链接和图片
desc = f"""<p>【后台环境】正式</p><p>【分支】发布分支</p><p>【机型】{fk.random_element(elements=['小米10','iPhone15'])} Android13</p><p><br /></p><p>【复现步骤】</p><p>1. 升级 2.29 → 2.30</p><p>2. 使用帮助无法上传日志</p><p><br /></p><p>【预期结果】</p><p>日志可正常上传</p><p><br /></p><p>测试腾讯文档链接：</p><p>【腾讯文档】{fk.word()}复现测试报告</p><p><a href="{fake_doc_link()}" target="_blank" rel="noopener">{fake_doc_link()}</a></p><p><br /></p><p>复现截图：</p><p class="tox-clear-float"><img src="{fake_img_url()}" style="width: 80%;" /></p>"""
return {
"id": bug_id,
"title": f"[Android] 升级测试使用帮助无法上传日志",
"description": desc,
"priority": random.choice(["medium", "high", "low"]),
"severity": random.choice(["normal", "serious", "slight"]),
"status": random.choice(["new", "closed", "resolved"]),
"reporter": "TAPD",
"created": created_time,
"resolved": modified_time if random.choice([True, False]) else None,
"closed": modified_time if random.choice([True, False]) else None,
"modified": modified_time,
"lastmodify": fk.name(),
"version_report": f"版本{random.randint(1, 3)}",
"iteration_id": random.choice(["1137857678001000003", "1137857678001000004", "1137857678001000005"]),
"resolution": random.choice(["fixed", "duplicate", "wontfix", ""]),
"regression_number": str(random.randint(0, 3)),
"release_id": "0",
"verify_time": modified_time if random.choice([True, False]) else None,
"template_id": random.choice(["0", "1137857678001000013"]),
"label": random.choice(["", "有风险", "阻塞"]),
"size": str(random.randint(1, 3)),
"effort": str(random.randint(1, 3)),
"effort_completed": "0",
"exceed": str(random.randint(-3, 3)),
"remain": str(random.randint(0, 3)),
"secret_root_id": "0",
"custom_plan_field_1": "0",
"custom_plan_field_2": "0",
"custom_plan_field_3": "0",
"custom_plan_field_4": "0",
"custom_plan_field_5": "0",
"custom_plan_field_6": "0",
"custom_plan_field_7": "0",
"custom_plan_field_8": "0",
"custom_plan_field_9": "0",
"custom_plan_field_10": "0",
"priority_label": random.choice(["medium", "high", "low"]),
"workspace_id": "37857678"
}
# ——— 入口 ———
def generate(n_story_A=300, n_story_B=200,
n_bug_A=400, n_bug_B=300, path="local_data/msg_from_fetcher.json"):
"""
生成TAPD测试数据
参数:
n_story_A: 算法策略类需求数量
n_story_B: 功能决策类需求数量
n_bug_A: 简单缺陷数量
n_bug_B: 详细缺陷数量
path: 输出文件路径
"""
# 如果不是绝对路径，转换为相对于项目根目录的路径
if not pathlib.Path(path).is_absolute():
base_dir = pathlib.Path(__file__).parent.parent
path = str(base_dir / path)
# 生成需求和缺陷数据（已经包含完整的字段结构）
stories = [fake_demand_A() for _ in range(n_story_A)] + [fake_demand_B() for _ in range(n_story_B)]
bugs = [fake_bug_A() for _ in range(n_bug_A)] + [fake_bug_B() for _ in range(n_bug_B)]
# 构建与TAPD API一致的数据格式
data_to_save = {
'stories': stories,
'bugs': bugs
}
# 确保目标目录存在
# 使用统一的FileManager保存数据
file_manager = get_file_manager()
# 强制覆盖写入文件（确保不是追加模式）
try:
file_manager.save_json_data(data_to_save, path)
print(f"[SUCCESS] Generated {len(stories)} stories and {len(bugs)} bugs -> {path}")
except Exception as e:
# 如果出现问题，抛出详细错误
raise RuntimeError(f"Failed to write file {path}: {str(e)}")
# ——— 提供给MCP工具调用的异步函数 ———
async def generate_fake_tapd_data(n_story_A: int = 50, n_story_B: int = 30,
n_bug_A: int = 40, n_bug_B: int = 20,
output_path: str = "local_data/msg_from_fetcher.json") -> dict:
"""
生成模拟TAPD数据的异步函数，供MCP工具调用
参数:
n_story_A: 算法策略类需求数量
n_story_B: 功能决策类需求数量
n_bug_A: 简单缺陷数量
n_bug_B: 详细缺陷数量
output_path: 输出文件路径
返回:
生成结果字典
"""
try:
# 如果不是绝对路径，转换为相对于项目根目录的路径
if not pathlib.Path(output_path).is_absolute():
base_dir = pathlib.Path(__file__).parent.parent
output_path = str(base_dir / output_path)
generate(n_story_A, n_story_B, n_bug_A, n_bug_B, output_path)
return {
"status": "success",
"message": f"成功生成 {n_story_A + n_story_B} 个需求和 {n_bug_A + n_bug_B} 个缺陷",
"details": {
"stories": {
"algorithm_strategy": n_story_A,
"functional_decision": n_story_B,
"total": n_story_A + n_story_B
},
"bugs": {
"simple": n_bug_A,
"detailed": n_bug_B,
"total": n_bug_A + n_bug_B
},
"output_path": output_path
}
}
except Exception as e:
return {
"status": "error",
"message": f"生成数据失败: {str(e)}"
}
if __name__ == "__main__":
generate()