把测试执行时间砍掉80%？这5个脚本优化技巧，让我从加班到准点下班_千锋教育

# 错误做法：硬等5秒
import time
time.sleep(5)  # 无论元素是否出现，都等5秒

# 正确做法：显式等待，最多等10秒，元素出现就立即执行
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from selenium.webdriver.common.by import By

element = WebDriverWait(driver, 10).until(
    EC.presence_of_element_located((By.ID, "submit-button"))
)

# 安装插件
pip install pytest-xdist

# 用4个进程并行运行测试
pytest -n 4 test_suite.py

# 自动检测CPU核心数，全量利用
pytest -n auto test_suite.py

import pytest
import csv

# 读取测试数据
def load_test_data():
    with open('test_data.csv', 'r') as f:
        reader = csv.DictReader(f)
        return [row for row in reader]

# 数据驱动测试
@pytest.mark.parametrize("data", load_test_data())
def test_login(data):
    username = data['username']
    password = data['password']
    expected_result = data['expected_result']
    
    # 执行登录操作
    result = login(username, password)
    
    # 断言
    assert result == expected_result

from sklearn.ensemble import IsolationForest
import numpy as np

# 假设测试数据：每行是 [执行时间(秒), 错误码(0表示无), 响应状态(1成功,0失败)]
X_train = np.array([
    [1.2, 0, 1],   # 正常
    [2.5, 0, 1],   # 正常
    [15.3, 404, 0], # 异常
    [0.8, 0, 1],   # 正常
    [20.1, 500, 0]  # 异常
])

# 训练模型
model = IsolationForest(contamination=0.2)  # 假设20%数据为异常
model.fit(X_train)

# 预测新测试结果
new_test = np.array([[3.0, 0, 1], [18.0, 503, 0]])
predictions = model.predict(new_test)
# -1表示异常，1表示正常
print(predictions)  # 输出：[1, -1] 第二个测试结果异常

# conftest.py
import pytest
import time

@pytest.hookimpl(tryfirst=True, hookwrapper=True)
def pytest_runtest_protocol(item):
    start_time = time.time()
    yield
    end_time = time.time()
    duration = end_time - start_time
    if duration > 10:  # 超过10秒的用例自动标记为慢
        print(f"警告：用例 {item.name} 执行耗时 {duration:.2f} 秒")