- name
- test-data-gen
- description
- >
Test Data Generator
Generate realistic, structured test data for testing.
Quick Generation with Python Faker
Reference: https://faker.readthedocs.io/en/master/
Install
pip install fakerChinese Locale Data
from faker import Faker
fake = Faker('zh_CN')
# Basic personal info
print(fake.name()) # 张三
print(fake.phone_number()) # 13800138000
print(fake.address()) # 北京市朝阳区...
print(fake.company()) # XX科技有限公司
print(fake.email()) # zhangsan@example.com
print(fake.ssn()) # 身份证号 (18位)
print(fake.credit_card_number()) # 银行卡号
# Date/time
print(fake.date_of_birth(minimum_age=18, maximum_age=65))
print(fake.date_between(start_date='-1y', end_date='today'))Batch Generation Script
Use scripts/gen_data.py for batch data generation:
# Generate 100 users as JSON
python3 scripts/gen_data.py --type users --count 100 --format json --output users.json
# Generate 500 orders as CSV
python3 scripts/gen_data.py --type orders --count 500 --format csv --output orders.csv
# Generate SQL INSERT statements
python3 scripts/gen_data.py --type users --count 50 --format sql --table users --output seed.sqlData Generation Patterns
User Data
from faker import Faker
import json, random
fake = Faker('zh_CN')
def gen_user(uid):
return {
"id": uid,
"name": fake.name(),
"email": fake.ascii_free_email(),
"phone": fake.phone_number(),
"id_card": fake.ssn(),
"gender": random.choice(["male", "female"]),
"birth_date": str(fake.date_of_birth(minimum_age=18, maximum_age=60)),
"address": fake.address(),
"created_at": str(fake.date_time_between(start_date='-2y')),
"status": random.choices(["active", "inactive", "banned"],
weights=[85, 10, 5])[0],
}
users = [gen_user(i) for i in range(1, 101)]
print(json.dumps(users, ensure_ascii=False, indent=2))Order Data (with foreign keys)
def gen_order(oid, user_ids, product_ids):
qty = random.randint(1, 10)
price = round(random.uniform(9.9, 999.9), 2)
return {
"id": oid,
"user_id": random.choice(user_ids),
"product_id": random.choice(product_ids),
"quantity": qty,
"unit_price": price,
"total": round(qty * price, 2),
"status": random.choices(
["pending", "paid", "shipped", "delivered", "cancelled"],
weights=[10, 20, 25, 40, 5])[0],
"created_at": str(fake.date_time_between(start_date='-6m')),
"address": fake.address(),
}Boundary & Edge Case Data
Always include these special values in test datasets:
EDGE_CASES = {
"string": [
"", # empty
" ", # whitespace only
"a" * 256, # max length
"a" * 257, # over max length
"<script>alert(1)</script>", # XSS
"' OR 1=1 --", # SQLi
"张三\李四", # null byte
"🔍 emoji test 🎉", # emoji
"Ñoño café résumé", # unicode accents
],
"number": [0, -1, 1, 2147483647, -2147483648, 0.001, 99999999.99],
"date": ["1970-01-01", "2099-12-31", "2000-02-29", "2001-02-29"], # leap year
"phone": ["13800138000", "19900001111", "12345678901", "1380013800"], # boundary
}Output Formats
SQL INSERT
def to_sql(users, table="users"):
cols = users[0].keys()
lines = [f"INSERT INTO {table} ({','.join(cols)}) VALUES"]
for i, u in enumerate(users):
vals = []
for v in u.values():
if v is None:
vals.append("NULL")
elif isinstance(v, (int, float)):
vals.append(str(v))
else:
vals.append(f"'{str(v).replace(chr(39), chr(39)*2)}'")
sep = "," if i < len(users) - 1 else ";"
lines.append(f" ({','.join(vals)}){sep}")
return "\
".join(lines)CSV
import csv
def to_csv(data, filepath):
with open(filepath, 'w', newline='', encoding='utf-8-sig') as f:
writer = csv.DictWriter(f, fieldnames=data[0].keys())
writer.writeheader()
writer.writerows(data)JSON Fixture
def to_json(data, filepath):
with open(filepath, 'w', encoding='utf-8') as f:
json.dump(data, f, ensure_ascii=False, indent=2, default=str)Chinese ID Card Validation
Reference: GB 11643-1999 (公民身份号码标准)
def validate_id_card(id_number: str) -> bool:
"""Validate Chinese 18-digit ID card number per GB 11643-1999."""
if len(id_number) != 18:
return False
weights = [7, 9, 10, 5, 8, 4, 2, 1, 6, 3, 7, 9, 10, 5, 8, 4, 2]
check_codes = '10X98765432'
try:
total = sum(int(id_number[i]) * weights[i] for i in range(17))
return check_codes[total % 11] == id_number[17].upper()
except (ValueError, IndexError):
return False
def gen_valid_id_card():
"""Generate a valid Chinese ID card number with correct checksum."""
import random
# Area codes (sample: Beijing, Shanghai, Guangdong)
areas = ['110101', '310101', '440106', '330102', '510104']
area = random.choice(areas)
birth = fake.date_of_birth(minimum_age=18, maximum_age=60).strftime('%Y%m%d')
seq = f"{random.randint(0, 999):03d}"
base = area + birth + seq
weights = [7, 9, 10, 5, 8, 4, 2, 1, 6, 3, 7, 9, 10, 5, 8, 4, 2]
check_codes = '10X98765432'
total = sum(int(base[i]) * weights[i] for i in range(17))
return base + check_codes[total % 11]Data Masking / Desensitization
For copying production data to test environments:
def mask_phone(phone: str) -> str:
"""138****8000"""
if len(phone) >= 11:
return phone[:3] + "****" + phone[7:]
return "***"
def mask_id_card(id_card: str) -> str:
"""110101****0001"""
if len(id_card) >= 18:
return id_card[:6] + "********" + id_card[14:]
return "***"
def mask_name(name: str) -> str:
"""张*"""
if len(name) >= 2:
return name[0] + "*" * (len(name) - 1)
return "*"
def mask_email(email: str) -> str:
"""z***@example.com"""
local, domain = email.split("@")
return local[0] + "***@" + domain
def mask_bank_card(card: str) -> str:
"""6222 **** **** 1234"""
if len(card) >= 16:
return card[:4] + " **** **** " + card[-4:]
return "****"Tips
- Always use
ensure_ascii=Falsefor Chinese JSON output - Use
utf-8-sigencoding for CSV files opened in Excel - Set
Faker.seed(42)for reproducible test data - Mix normal data (90%) with edge cases (10%) for realistic coverage
- Include data relationships (FK references) when generating related tables
- For large datasets (>10k rows), use batch inserts and transactions