实测Copilot：代码补全效果全面评测

# Copilot 产品级评测指标计算演示 # 1. 模拟两组开发者数据（使用组 vs 对照组）users = {    'copilot': {        'task_time': [45, 50, 42, 48, 55, 40, 52, 47, 44, 51],        'accept_rate': 0.35,        'chars_accepted': 350,        'chars_suggested': 1000,        'satisfaction': 4.2    },    'control': {        'task_time': [60, 65, 58, 70, 62, 68, 64, 59, 67, 63],        'accept_rate': 0,        'chars_accepted': 0,        'chars_suggested': 0,        'satisfaction': 3.5    }} # 2. 任务完成时间对比import numpy as npfrom scipy import stats copilot_times = users['copilot']['task_time']control_times = users['control']['task_time'] mean_copilot = np.mean(copilot_times)mean_control = np.mean(control_times)time_reduction = (mean_control - mean_copilot) / mean_control t_stat, p_value = stats.ttest_ind(control_times, copilot_times)print(f'Copilot组平均时间: {mean_copilot:.1f}分钟')print(f'对照组平均时间: {mean_control:.1f}分钟')print(f'时间减少: {time_reduction:.1%}')print(f't检验: t={t_stat:.2f}, p={p_value:.4f}')print('效率提升显著!' if p_value < 0.05 else '效率提升不显著') # 3. 字符级接受率计算chars_accepted = users['copilot']['chars_accepted']chars_suggested = users['copilot']['chars_suggested']char_accept_rate = chars_accepted / chars_suggestedprint(f'\n字符级接受率: {char_accept_rate:.1%}') # 4. 满意度对比print(f'Copilot组满意度: {users["copilot"]["satisfaction"]:.1f}/5.0')print(f'对照组满意度: {users["control"]["satisfaction"]:.1f}/5.0') # 5. 综合评估指标def overall_score(time_reduction, char_accept_rate, satisfaction):    # 综合评分：效率40% + 采纳30% + 满意度30%    return time_reduction * 0.4 + char_accept_rate * 0.3 + (satisfaction / 5.0) * 0.3 score = overall_score(time_reduction, char_accept_rate, users['copilot']['satisfaction'])print(f'\n综合评估得分: {score:.3f}')print('推荐推广' if score > 0.6 else '谨慎评估') # 6. 样本量校验（简单功效分析）effect_size = (mean_control - mean_copilot) / np.std(control_times + copilot_times, ddof=1)print(f'\n效应量(Cohen d): {effect_size:.3f}')print('效应量小' if effect_size < 0.5 else '效应量中' if effect_size < 0.8 else '效应量大')