AI 测试全体系详解：自动化测试框架 + 智能缺陷检测 + A/B 测试优化

用户层 → AI能力层 → 自动化执行底座 → 多端适配层 → 测试资源层 → 执行结果层 → 反馈优化层

import time
import cv2
import numpy as np
from playwright.sync_api import sync_playwright
from sklearn.ensemble import RandomForestClassifier
import pytest
from pytest_html import extras

# AI核心模块：异常分类器
X_train = np.array([[1, 10, 0, 0], [2, 5, 1, 0], [3, 2, 0, 1], [4, 8, 1, 1], [5, 3, 0, 0]])
y_train = np.array([0, 1, 2, 3, 4])
ai_exception_classifier = RandomForestClassifier(n_estimators=100)
ai_exception_classifier.fit(X_train, y_train)

# 异常处理策略映射
exception_strategy = {
    0: lambda page: page.wait_for_load_state("networkidle", timeout=20000),
    1: lambda page: page.evaluate("document.querySelector('button').scrollIntoView()"),
    2: lambda page: page.click("//div[contains(@class,'popup-close')]"),
    3: lambda page: page.reload(),
    4: lambda page: page.goto(page.url, wait_until="domcontentloaded")
}

# AI核心模块：CV智能元素定位
def ai_find_element_by_image(page, target_img_path, threshold=0.8):
    page.screenshot(path="current_page.png", full_page=True)
    src_img = cv2.imread("current_page.png")
    tar_img = cv2.imread(target_img_path)
    res = cv2.matchTemplate(src_img, tar_img, cv2.TM_CCOEFF_NORMED)
    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
    if max_val >= threshold:
        h, w = tar_img.shape[:2]
        x = max_loc[0] + w // 2
        y = max_loc[1] + h // 2
        return (x, y)
    return None

# AI核心模块：NLP转测试用例执行器
def ai_run_case_by_nlp(page, nlp_instruction):
    instruction = nlp_instruction.lower()
    if "打开" in instruction and "url" in instruction:
        url = instruction.split("url")[1].strip()
        page.goto(url, wait_until="networkidle")
    elif "点击" in instruction:
        if "图片" in instruction:
            img_path = instruction.split("图片")[1].strip()
            pos = ai_find_element_by_image(page, img_path)
            if pos:
                page.click(f"[{pos[0]},{pos[1]}]")
    elif "输入" in instruction:
        text = instruction.split("输入")[1].strip()
        page.keyboard.type(text)
    elif "断言" in instruction:
        assert_text = instruction.split("断言")[1].strip()
        assert assert_text in page.content()

import cv2
import numpy as np
from PIL import Image, ImageDraw, ImageFont

class AICVUIDefectDetector:
    def __init__(self, base_img_path, test_img_path, threshold=30):
        self.base_img = cv2.imread(base_img_path)
        self.test_img = cv2.imread(test_img_path)
        self.threshold = threshold
        self.defect_areas = []
        self.defect_types = []

    def ai_pixel_compare(self):
        base_gray = cv2.cvtColor(self.base_img, cv2.COLOR_BGR2GRAY)
        test_gray = cv2.cvtColor(self.test_img, cv2.COLOR_BGR2GRAY)
        diff = cv2.absdiff(base_gray, test_gray)
        ret, diff_bin = cv2.threshold(diff, self.threshold, 255, cv2.THRESH_BINARY)
        kernel = np.ones((3,3), np.uint8)
        diff_bin = cv2.morphologyEx(diff_bin, cv2.MORPH_OPEN, kernel)
        contours, _ = cv2.findContours(diff_bin, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        for cnt in contours:
            if cv2.contourArea(cnt) > 10:
                x, y, w, h = cv2.boundingRect(cnt)
                self.defect_areas.append((x, y, w, h))
        return len(self.defect_areas) > 0

    def generate_defect_report(self, save_path="ui_defect_report.png"):
        img = Image.fromarray(cv2.cvtColor(self.test_img, cv2.COLOR_BGR2RGB))
        draw = ImageDraw.Draw(img)
        for i, area in enumerate(self.defect_areas):
            x, y, w, h = area
            draw.rectangle([x, y, x+w, y+h], outline="red", width=2)
        img.save(save_path)

import re
import jieba
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB

class AINLPLogDefectDetector:
    def __init__(self):
        self.log_data = [
            "2026-01-14 10:00:00 INFO: 用户登录成功",
            "2026-01-14 10:01:00 ERROR: NullPointerException at com.test.UserController.login",
            "2026-01-14 10:02:00 WARNING: 数据库连接池数量不足",
        ]
        self.log_labels = [0, 1, 2, 1, 0]  # 0=正常 1=报错 2=警告
        self.vectorizer = TfidfVectorizer()
        self.classifier = MultinomialNB()
        self._train_model()

    def ai_analyse_log(self, log_file_path):
        defect_logs = []
        with open(log_file_path, "r", encoding="utf-8") as f:
            for line_num, line in enumerate(f.readlines()):
                seg_line = " ".join(jieba.cut(line))
                X_test = self.vectorizer.transform([seg_line])
                label = self.classifier.predict(X_test)[0]
                if label in [1, 2]:
                    defect_logs.append({"行号": line_num+1, "日志内容": line, "缺陷类型": label})
        return defect_logs

你现在是一位资深的软件测试架构师+后端开发专家+前端开发专家，具备10年的缺陷根因定位和修复经验。
请你基于以下缺陷信息，完成2个核心任务：
1. 精准定位该缺陷的根因
2. 给出具体、可落地、分步的修复建议

缺陷信息：
【缺陷类型】UI缺陷
【缺陷详情】电商首页的商品列表模块，在手机端分辨率为375x667时，第三个商品的图片被遮挡
【测试环境】Chrome 120、Android 14、Vue3+Vant4框架

import pandas as pd
import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt
from sklearn.ensemble import GradientBoostingRegressor

class AIAnalysisABTest:
    def __init__(self, data_path):
        self.data = pd.read_csv(data_path)
        self.versions = self.data["version"].unique()
        self.ctr_dict = {}
        self.cvr_dict = {}
        self.model = GradientBoostingRegressor(n_estimators=100)

    def ai_calculate_core_metrics(self):
        for ver in self.versions:
            ver_data = self.data[self.data["version"] == ver]
            self.ctr_dict[ver] = round(ver_data["click"].mean() * 100, 2)
            self.cvr_dict[ver] = round(ver_data[ver_data["click"]==1]["convert"].mean() * 100, 2)
        return self.ctr_dict, self.cvr_dict

    def ai_dynamic_traffic_allocation(self):
        traffic_allocation = {}
        total_score = 0
        for ver in self.versions:
            score = self.ctr_dict[ver] * 0.5 + self.cvr_dict[ver] * 0.4
            traffic_allocation[ver] = score
            total_score += score
        return {k: round(v/total_score*100, 2) for k, v in traffic_allocation.items()}

    def ai_statistical_significance_test(self):
        a_data = self.data[self.data["version"] == "A"]["convert"]
        for ver in self.versions:
            if ver == "A": continue
            b_data = self.data[self.data["version"] == ver]["convert"]
            t_stat, p_value = stats.ttest_ind(a_data, b_data, equal_var=False)
            conclusion = "差异显著" if p_value < 0.05 else "差异不显著"
            print(f"版本A VS 版本{ver}：p_value={round(p_value, 4)}，{conclusion}")

    def ai_final_decision(self):
        decision_score = {}
        for ver in self.versions:
            decision_score[ver] = self.ctr_dict[ver]*0.3 + self.cvr_dict[ver]*0.6
        best_version = max(decision_score, key=decision_score.get)
        print(f"最优版本：{best_version}")
        return best_version