《告别脆弱的 E2E：用 AI Agent 构建自愈式端到端测试框架》

# 伪代码：AI Agent 的自愈式元素查找
class SelfHealingLocator:
    def __init__(self, agent, intent: str):
        self.agent = agent
        self.intent = intent  # e.g., "the main submit button"
        self.strategies = [
            ("data-testid", self._generate_data_testid),
            ("text_content", self._generate_by_text),
            ("visual_ai", self._generate_by_screenshot),  # 未来可扩展
        ]

    def find(self):
        page_source = self.agent.get_page_source()

        for strategy_name, generator in self.strategies:
            try:
                locator = generator(self.intent, page_source)
                element = self.agent.playwright_driver.find(locator)
                if element.is_visible():
                    # 成功！并将此映射关系缓存，供下次使用
                    self._cache_successful_mapping(self.intent, locator)
                    return element
            except Exception as e:
                print(f"Strategy {strategy_name} failed: {e}")
                continue

        raise ElementNotFoundException(f"Could not locate element for intent: {self.intent}")

    def _generate_data_testid(self, intent, page_source):
        # 基于团队约定，从意图推导可能的 data-testid
        return f"[data-testid='{self._intent_to_id(intent)}']"

    def _generate_by_text(self, intent, page_source):
        # 调用 LLM，根据意图和页面源码生成基于文本的 XPath
        prompt = f"""
        Given the user intent: '{intent}',
        and the following HTML snippet:
        {page_source[:2000]}...
        Please generate a robust XPath that uniquely identifies the target element.
        """
        xpath = self.agent.llm.chat(prompt)
        return f"xpath={xpath}"

def diagnose_failure(agent, error):
    diagnostics = {
        "network": agent.check_network_latency(),
        "backend_health": agent.query_service_health("payment-service"),
        "test_data": agent.query_test_db("SELECT * FROM test_credit_cards WHERE is_active = true;"),
        "browser_console": agent.get_browser_console_logs(),
    }

    # 将诊断信息和错误信息一起发送给 LLM 进行分析
    analysis_prompt = f"""
    An E2E test failed with error: {error}.
    Here is the diagnostic context:
    - Network latency: {diagnostics['network']}
    - Payment service health: {diagnostics['backend_health']}
    - Active test cards: {len(diagnostics['test_data'])}
    - Browser console errors: {diagnostics['browser_console']}

    Please analyze the root cause. Is this likely a:
    A) Product bug
    B) Test environment issue (e.g., service down)
    C) Test data issue (e.g., no valid card)
    D) Flaky test (e.g., network timeout)

    Respond with ONLY the letter (A, B, C, or D).
    """
    root_cause = agent.llm.chat(analysis_prompt).strip()

    if root_cause == "C":
        # 如果是数据问题，尝试修复
        agent.provision_new_test_card()
        return "RETRY"
    elif root_cause in ["B", "D"]:
        return "SKIP_WITH_WARNING"
    else:
        return "FAIL_WITH_REPORT"