基于Python Playwright进行前端性能测试的脚本实现

def _initialize_browser(self):
    """初始化浏览器环境"""
    self.playwright = sync_playwright().start()  # 启动Playwright
    self.browser = self.playwright.chromium.launch(headless=False)  # 启动Chromium浏览器
    self.context = self.browser.new_context(
        ignore_https_errors=True,  # 忽略HTTPS错误
        bypass_csp=True,  # 绕过内容安全策略
        extra_http_headers={  # 设置HTTP头禁用缓存
            'Cache-Control': 'no-store, no-cache, must-revalidate',
            'Pragma': 'no-cache'
        }
    )
    self.page = self.context.new_page()  # 创建新页面
    self.page.goto('about:blank')  # 导航到空白页
    self.page.route('**', lambda route: route.continue_())  # 拦截所有请求并继续

def _collect_performance_metrics(self) -> Dict[str, Any]:
    """收集性能指标数据"""
    start_time = time.time()  # 记录开始时间
    self.page.goto(self.test_url, wait_until="networkidle")  # 导航到目标URL
    end_time = time.time()  # 记录结束时间

    # 基础指标：页面加载总时间
    self.metrics_data = {
        'load_time_seconds': end_time - start_time,
    }

    # 通过浏览器API获取详细性能指标
    browser_metrics = self.page.evaluate('''() => {
        try {
            const navEntries = performance.getEntriesByType("navigation");
            const resources = performance.getEntriesByType("resource") || [];
            return {
                navigation: navEntries.length > 0 ? navEntries[0].toJSON() : {},
                resources: resources.map(r => r.toJSON())
            };
        } catch (e) {
            return { navigation: {}, resources: [] };
        }
    }''')

def _analyze_resources(self, resources: List[Dict]) -> Dict[str, Any]:
    """分析资源加载情况"""
    resource_types = defaultdict(list)  # 按类型分类资源
    slow_resources = []  # 存储加载缓慢的资源

    # 分类和分析资源
    for res in resources:
        res_type = res.get('initiatorType', 'other')
        resource_types[res_type].append(res)
        duration = res.get('duration')
        if isinstance(duration, (int, float)) and duration > 500:
            slow_resources.append({
                'name': res.get('name', 'unknown'),
                'type': res_type,
                'duration': duration
            })

    # 计算统计信息

def _analyze_metrics(self):
    """分析关键性能指标"""
    nav_entry = self.metrics_data.get('navigation', {})

    self.metrics_data['analysis'] = {
        'dom_ready_time': nav_entry.get('domContentLoadedEventEnd'),  # DOM准备就绪时间
        'full_load_time': nav_entry.get('loadEventEnd'),  # 完整加载时间
        'first_byte': nav_entry.get('responseStart', 0) - nav_entry.get('requestStart', 0)
        if all(k in nav_entry for k in ['responseStart', 'requestStart'])
        else None,  # 首字节时间(TTFB)
        'page_render_time': nav_entry.get('domComplete', 0) - nav_entry.get('domLoading', 0)
        if all(k in nav_entry for k in ['domComplete', 'domLoading'])
        else None,  # 页面渲染时间
        'resource_count': self.resource_analysis.get('total_resources', 0),  # 资源总数
        'avg_resource_load_time': self.resource_analysis.get('avg_resource_load_time', 0)
    }

def _generate_recommendations(self) -> List[str]:
    """根据分析结果生成优化建议"""
    recommendations = []
    analysis = self.metrics_data.get('analysis', {})
    resources = self.resource_analysis

    # 资源数量过多建议
    if analysis.get('resource_count', 0) > 50:
        recommendations.append(
            f"资源数量较多({analysis['resource_count']}个)，建议合并CSS/JS文件，使用雪碧图减少图片资源"
        )

    # 慢资源优化建议
    slow_resources = [
        r for r in resources.get('top_slowest', [])
        if r.get('duration', 0) > 1000
    ]

if __name__ == "__main__":
    # 创建测试实例并运行测试
    tester = PerformanceTester(url="https://example.com")
    tester.run_test()