Chord视频分析自动化测试：Python脚本编写实战-平芜编程栈

Chord视频分析自动化测试：Python脚本编写实战

1. 为什么需要为Chord视频分析工具编写自动化测试

在实际项目中，Chord视频分析工具被广泛用于理解视频中的时空关系——比如识别物体在画面中的移动轨迹、判断事件发生的时间顺序、分析人物之间的交互模式等。这类工具一旦部署到生产环境，往往需要处理大量视频数据，任何功能异常都可能导致分析结果偏差，进而影响后续决策。

我刚开始接触Chord时也走过弯路：手动验证每个功能点既耗时又容易遗漏边界情况。有一次，团队在升级Chord版本后，发现时间戳解析模块在处理跨天视频时会出现偏移，但这个bug直到上线三天后才被业务方反馈。当时我就意识到，必须建立一套可靠的自动化测试体系。

这套测试不是为了追求覆盖率数字，而是要真实模拟使用场景。比如，当用户上传一段30秒的监控视频，要求识别“人从左向右穿过画面”，测试脚本就要验证Chord是否能准确返回起始帧、结束帧、运动方向和置信度。这种贴近实际的验证方式，比单纯检查函数返回值更有意义。

对于刚接触软件测试的朋友来说，不必担心门槛太高。我们用的是Python生态中最友好的unittest框架，语法简洁，学习成本低。更重要的是，测试代码本身也是对Chord功能最好的文档——当你看到一个测试用例，就能立刻明白这个功能该怎么用、预期结果是什么。

2. 环境准备与基础测试框架搭建

开始写测试前，我们需要先确保开发环境就绪。Chord视频分析工具通常以Python包形式提供，所以第一步是安装它以及必要的测试依赖。

# 创建独立的虚拟环境（推荐） python -m venv chord_test_env source chord_test_env/bin/activate # Linux/Mac # chord_test_env\Scripts\activate # Windows # 安装Chord工具（假设已发布到PyPI） pip install chord-video-analyzer # 安装测试相关库 pip install pytest pytest-cov mock

接下来创建基础测试框架。我们在项目根目录下新建tests/文件夹，并添加第一个测试文件test_chord_basic.py：

# tests/test_chord_basic.py import unittest from unittest.mock import patch, MagicMock import tempfile import os # 导入Chord的核心分析类 from chord_video_analyzer import VideoAnalyzer, AnalysisResult class TestChordBasic(unittest.TestCase): """Chord视频分析工具的基础功能测试""" def setUp(self): """每个测试方法执行前运行，初始化测试环境""" self.analyzer = VideoAnalyzer() # 创建临时测试视频路径（实际项目中可使用预存的小视频） self.test_video_path = "test_sample.mp4" def tearDown(self): """每个测试方法执行后运行，清理资源""" if os.path.exists(self.test_video_path): os.remove(self.test_video_path) def test_analyzer_initialization(self): """测试分析器初始化是否正常""" self.assertIsNotNone(self.analyzer) self.assertTrue(hasattr(self.analyzer, 'analyze')) self.assertTrue(hasattr(self.analyzer, 'get_supported_features')) def test_supported_features_list(self): """测试支持的功能列表是否包含关键能力""" features = self.analyzer.get_supported_features() self.assertIsInstance(features, list) self.assertIn('temporal_reasoning', features) self.assertIn('spatial_tracking', features) self.assertIn('event_detection', features)

这个基础框架已经包含了几个关键要素：使用setUp()和tearDown()管理测试状态，用assertIsNotNone()和assertIn()进行断言，以及清晰的测试方法命名。注意我们没有真正加载视频文件，而是通过结构化的方式验证Chord的基本能力。

运行测试也很简单：

python -m unittest tests/test_chord_basic.py -v

如果看到绿色的OK输出，说明环境搭建成功，基础测试通过。这一步看似简单，却是整个自动化测试体系的地基——只有确保基础功能稳定，才能继续构建更复杂的测试场景。

3. 核心功能的单元测试实现

Chord视频分析工具的核心价值在于其时空理解能力，所以我们需要重点覆盖三个关键功能模块：时间推理、空间追踪和事件检测。每个模块都需要设计既能验证正常流程、又能捕捉异常情况的测试用例。

3.1 时间推理功能测试

时间推理是Chord最独特的功能之一，它能理解视频中事件发生的先后顺序和持续时间。我们来编写一个典型的测试用例：

# tests/test_chord_temporal.py import unittest from unittest.mock import patch, MagicMock from chord_video_analyzer import VideoAnalyzer class TestChordTemporalReasoning(unittest.TestCase): def setUp(self): self.analyzer = VideoAnalyzer() @patch('chord_video_analyzer.VideoAnalyzer._load_video_metadata') def test_temporal_reasoning_normal_case(self, mock_load_meta): """测试正常时间推理场景""" # 模拟视频元数据：30秒，30fps mock_load_meta.return_value = { 'duration': 30.0, 'fps': 30.0, 'frame_count': 900 } # 模拟分析结果 mock_result = MagicMock() mock_result.temporal_events = [ {'event': 'person_enters', 'start_frame': 45, 'end_frame': 120}, {'event': 'object_picked_up', 'start_frame': 200, 'end_frame': 215}, {'event': 'person_exits', 'start_frame': 800, 'end_frame': 850} ] mock_result.get_timeline.return_value = [ '0-1.5s: person enters', '6.7-7.2s: object picked up', '26.7-28.3s: person exits' ] with patch('chord_video_analyzer.VideoAnalyzer._run_temporal_analysis') as mock_analyze: mock_analyze.return_value = mock_result # 执行分析 result = self.analyzer.analyze("test.mp4", features=['temporal_reasoning']) # 验证结果 self.assertEqual(len(result.temporal_events), 3) self.assertIn('person_enters', [e['event'] for e in result.temporal_events]) self.assertGreater(result.temporal_events[0]['end_frame'], result.temporal_events[0]['start_frame']) def test_temporal_reasoning_edge_cases(self): """测试时间推理的边界情况""" # 测试空视频路径 with self.assertRaises(ValueError) as context: self.analyzer.analyze("", features=['temporal_reasoning']) self.assertIn("video path cannot be empty", str(context.exception)) # 测试不支持的视频格式 with self.assertRaises(ValueError) as context: self.analyzer.analyze("test.xyz", features=['temporal_reasoning']) self.assertIn("unsupported video format", str(context.exception))

这个测试用例展示了如何用@patch装饰器模拟外部依赖，避免真实读取视频文件。我们验证了三个关键点：事件数量是否正确、事件类型是否包含预期值、时间范围是否合理。同时，边缘情况测试确保了错误处理的健壮性。

3.2 空间追踪功能测试

空间追踪关注物体在画面中的位置变化，这对理解行为模式至关重要：

# tests/test_chord_spatial.py import unittest from unittest.mock import patch, MagicMock from chord_video_analyzer import VideoAnalyzer, BoundingBox class TestChordSpatialTracking(unittest.TestCase): def setUp(self): self.analyzer = VideoAnalyzer() @patch('chord_video_analyzer.VideoAnalyzer._detect_objects') def test_spatial_tracking_multiple_objects(self, mock_detect): """测试多物体空间追踪""" # 模拟检测到两个物体 mock_detect.return_value = [ { 'frame_id': 0, 'objects': [ BoundingBox(x=100, y=200, width=50, height=80, label='person'), BoundingBox(x=400, y=150, width=60, height=70, label='car') ] }, { 'frame_id': 30, 'objects': [ BoundingBox(x=150, y=220, width=50, height=80, label='person'), BoundingBox(x=420, y=160, width=60, height=70, label='car') ] } ] with patch('chord_video_analyzer.VideoAnalyzer._track_objects') as mock_track: mock_track.return_value = [ { 'object_id': 'obj_001', 'label': 'person', 'trajectory': [(100, 200), (150, 220)], 'movement_distance': 53.85, 'movement_direction': 'right-down' }, { 'object_id': 'obj_002', 'label': 'car', 'trajectory': [(400, 150), (420, 160)], 'movement_distance': 22.36, 'movement_direction': 'right-down' } ] result = self.analyzer.analyze("test.mp4", features=['spatial_tracking']) # 验证追踪结果 self.assertEqual(len(result.spatial_tracks), 2) person_track = result.spatial_tracks[0] self.assertEqual(person_track['label'], 'person') self.assertAlmostEqual(person_track['movement_distance'], 53.85, places=2) self.assertEqual(person_track['movement_direction'], 'right-down') def test_spatial_tracking_occlusion_handling(self): """测试遮挡情况下的空间追踪""" # 模拟物体被短暂遮挡的场景 with patch('chord_video_analyzer.VideoAnalyzer._detect_objects') as mock_detect: mock_detect.side_effect = [ # 第1帧：检测到person [{'frame_id': 0, 'objects': [BoundingBox(100, 200, 50, 80, 'person')]}], # 第10帧：person被遮挡，未检测到 [{'frame_id': 10, 'objects': []}], # 第20帧：person重新出现 [{'frame_id': 20, 'objects': [BoundingBox(180, 210, 50, 80, 'person')]}] ] with patch('chord_video_analyzer.VideoAnalyzer._track_objects') as mock_track: mock_track.return_value = [{ 'object_id': 'obj_001', 'label': 'person', 'trajectory': [(100, 200), (180, 210)], 'occlusion_frames': 10 }] result = self.analyzer.analyze("test.mp4", features=['spatial_tracking']) self.assertEqual(result.spatial_tracks[0]['occlusion_frames'], 10)

这里我们特别关注了遮挡处理这一实际场景中的常见问题。在真实监控视频中，物体经常会被其他物体或画面元素暂时遮挡，好的空间追踪算法应该能保持ID一致性并记录遮挡时长。

3.3 事件检测功能测试

事件检测是将时空信息转化为业务可理解语义的关键步骤：

# tests/test_chord_event_detection.py import unittest from unittest.mock import patch, MagicMock from chord_video_analyzer import VideoAnalyzer, Event class TestChordEventDetection(unittest.TestCase): def setUp(self): self.analyzer = VideoAnalyzer() @patch('chord_video_analyzer.VideoAnalyzer._extract_features') def test_event_detection_complex_scenario(self, mock_extract): """测试复杂事件检测场景""" # 模拟特征提取结果：包含时间、空间、动作特征 mock_extract.return_value = { 'temporal_features': {'duration': 5.2, 'frequency': 1.2}, 'spatial_features': {'bounding_boxes': [(100, 150, 50, 80), (200, 160, 45, 75)]}, 'action_features': {'motion_vectors': [0.3, 0.8, 0.1], 'pose_changes': 3} } # 模拟事件检测结果 mock_events = [ Event( event_type='person_interaction', confidence=0.92, start_time=2.1, end_time=7.3, description='Two people approach each other and shake hands', related_objects=['person_001', 'person_002'] ), Event( event_type='object_transfer', confidence=0.87, start_time=4.5, end_time=5.8, description='Person hands an object to another person', related_objects=['person_001', 'person_002', 'object_001'] ) ] with patch('chord_video_analyzer.VideoAnalyzer._detect_events') as mock_detect: mock_detect.return_value = mock_events result = self.analyzer.analyze("test.mp4", features=['event_detection']) # 验证事件检测结果 self.assertEqual(len(result.detected_events), 2) self.assertEqual(result.detected_events[0].event_type, 'person_interaction') self.assertGreater(result.detected_events[0].confidence, 0.9) self.assertIn('shake hands', result.detected_events[0].description) def test_event_detection_low_confidence_filtering(self): """测试低置信度事件的过滤机制""" # 创建一个低置信度事件 low_conf_event = MagicMock() low_conf_event.confidence = 0.25 low_conf_event.event_type = 'false_positive' high_conf_event = MagicMock() high_conf_event.confidence = 0.85 high_conf_event.event_type = 'real_event' with patch('chord_video_analyzer.VideoAnalyzer._detect_events') as mock_detect: mock_detect.return_value = [low_conf_event, high_conf_event] # 设置置信度过滤阈值 result = self.analyzer.analyze( "test.mp4", features=['event_detection'], confidence_threshold=0.3 ) # 验证只有高置信度事件被保留 self.assertEqual(len(result.detected_events), 1) self.assertEqual(result.detected_events[0].event_type, 'real_event')

事件检测测试突出了业务价值：不仅检测出事件，还要给出可理解的描述。我们验证了事件类型、置信度和自然语言描述三个维度，确保Chord输出的结果可以直接用于业务系统或人工审核。

4. 异常场景与鲁棒性测试

在真实环境中，Chord视频分析工具面临的挑战远不止正常视频。网络传输可能损坏文件，用户可能上传格式不标准的视频，甚至故意构造恶意输入来测试系统边界。这些异常场景的测试，往往比功能测试更能体现系统的成熟度。

4.1 视频文件异常处理

我们首先测试各种视频文件异常情况：

# tests/test_chord_error_handling.py import unittest import tempfile import os from unittest.mock import patch, MagicMock from chord_video_analyzer import VideoAnalyzer class TestChordErrorHandling(unittest.TestCase): def setUp(self): self.analyzer = VideoAnalyzer() def test_corrupted_video_file(self): """测试损坏视频文件的处理""" # 创建一个空文件模拟损坏视频 with tempfile.NamedTemporaryFile(delete=False, suffix='.mp4') as tmp: tmp.write(b'') # 写入空内容 corrupted_path = tmp.name try: with self.assertRaises(RuntimeError) as context: self.analyzer.analyze(corrupted_path, features=['temporal_reasoning']) self.assertIn("corrupted", str(context.exception).lower()) finally: os.unlink(corrupted_path) def test_unsupported_resolution(self): """测试超高分辨率视频的处理""" # 模拟不支持的分辨率（如16K） with patch('chord_video_analyzer.VideoAnalyzer._get_video_info') as mock_info: mock_info.return_value = { 'width': 15360, # 16K宽度 'height': 8640, 'duration': 10.0 } with self.assertRaises(ValueError) as context: self.analyzer.analyze("test.mp4", features=['spatial_tracking']) self.assertIn("resolution too high", str(context.exception).lower()) def test_insufficient_memory_handling(self): """测试内存不足时的优雅降级""" # 模拟内存检查失败 with patch('chord_video_analyzer.VideoAnalyzer._check_system_resources') as mock_check: mock_check.return_value = False # 应该返回友好的错误信息而不是崩溃 result = self.analyzer.analyze("test.mp4", features=['temporal_reasoning']) # 验证返回了降级结果 self.assertTrue(hasattr(result, 'status')) self.assertEqual(result.status, 'degraded') self.assertIn('memory_limit_exceeded', result.warnings)

这些测试用例覆盖了生产环境中最常见的文件异常：损坏、格式不支持、分辨率超限等。关键是验证Chord不是简单地抛出技术异常，而是提供有意义的错误信息，并在可能的情况下进行优雅降级。

4.2 参数边界与非法输入测试

参数验证是防止系统被误用的重要防线：

# tests/test_chord_parameter_validation.py import unittest from chord_video_analyzer import VideoAnalyzer class TestChordParameterValidation(unittest.TestCase): def setUp(self): self.analyzer = VideoAnalyzer() def test_invalid_confidence_threshold(self): """测试置信度阈值的边界值""" # 小于0的阈值 with self.assertRaises(ValueError) as context: self.analyzer.analyze("test.mp4", confidence_threshold=-0.1) self.assertIn("must be between 0 and 1", str(context.exception)) # 大于1的阈值 with self.assertRaises(ValueError) as context: self.analyzer.analyze("test.mp4", confidence_threshold=1.5) self.assertIn("must be between 0 and 1", str(context.exception)) def test_invalid_time_range(self): """测试时间范围参数验证""" # 起始时间大于结束时间 with self.assertRaises(ValueError) as context: self.analyzer.analyze( "test.mp4", time_range=(10.0, 5.0) # 无效的时间范围 ) self.assertIn("start time must be less than end time", str(context.exception)) def test_too_many_parallel_threads(self): """测试并行线程数限制""" # 设置过大的线程数 with self.assertRaises(ValueError) as context: self.analyzer.analyze( "test.mp4", max_workers=1000 # 远超合理范围 ) self.assertIn("exceeds system limit", str(context.exception)) def test_malformed_prompt_input(self): """测试恶意构造的提示词输入""" # 测试SQL注入式输入（虽然Chord不直接处理SQL，但要防范通用攻击模式） malicious_prompt = "'; DROP TABLE videos; --" with self.assertRaises(ValueError) as context: self.analyzer.analyze( "test.mp4", prompt=malicious_prompt ) self.assertIn("invalid characters detected", str(context.exception))

这些测试体现了防御性编程思想。我们不仅验证正常参数，更关注那些可能被滥用的边界情况。特别是恶意输入测试，确保Chord不会因为用户输入而产生安全风险。

5. 性能基准测试与优化验证

自动化测试不仅要保证功能正确，还要确保性能满足业务需求。Chord视频分析工具在不同硬件配置上表现差异很大，我们需要建立一套性能基准测试来量化其表现。

5.1 基准测试框架

首先创建性能测试的基础框架：

# tests/performance/test_performance_baseline.py import unittest import time import statistics from unittest.mock import patch, MagicMock from chord_video_analyzer import VideoAnalyzer class TestChordPerformanceBaseline(unittest.TestCase): def setUp(self): self.analyzer = VideoAnalyzer() self.test_durations = [] def record_performance(self, test_name, duration_ms): """记录性能数据""" self.test_durations.append({ 'test': test_name, 'duration_ms': duration_ms, 'timestamp': time.time() }) print(f"{test_name}: {duration_ms:.2f}ms") def assert_performance_within_threshold(self, actual_ms, threshold_ms, test_name): """断言性能在阈值内""" if actual_ms > threshold_ms: self.fail(f"{test_name} took {actual_ms:.2f}ms, exceeding threshold of {threshold_ms}ms") def test_small_video_analysis_performance(self): """测试小视频（10秒）分析性能""" # 模拟10秒视频分析 start_time = time.time() with patch('chord_video_analyzer.VideoAnalyzer._load_video_metadata') as mock_meta: mock_meta.return_value = {'duration': 10.0, 'fps': 30.0} with patch('chord_video_analyzer.VideoAnalyzer._run_full_analysis') as mock_analyze: mock_analyze.return_value = MagicMock() # 执行分析 self.analyzer.analyze("small.mp4", features=['temporal_reasoning']) end_time = time.time() duration_ms = (end_time - start_time) * 1000 # 记录并验证性能 self.record_performance("small_video_analysis", duration_ms) self.assert_performance_within_threshold(duration_ms, 2000, "small_video_analysis") def test_medium_video_analysis_performance(self): """测试中等视频（60秒）分析性能""" start_time = time.time() with patch('chord_video_analyzer.VideoAnalyzer._load_video_metadata') as mock_meta: mock_meta.return_value = {'duration': 60.0, 'fps': 30.0} with patch('chord_video_analyzer.VideoAnalyzer._run_full_analysis') as mock_analyze: mock_analyze.return_value = MagicMock() self.analyzer.analyze("medium.mp4", features=['spatial_tracking']) end_time = time.time() duration_ms = (end_time - start_time) * 1000 self.record_performance("medium_video_analysis", duration_ms) self.assert_performance_within_threshold(duration_ms, 8000, "medium_video_analysis")

这个基准测试框架的关键特点是：记录实际执行时间、设置合理的性能阈值、提供清晰的性能报告。我们为不同长度的视频设置了不同的性能目标，这反映了真实业务场景的需求差异。

5.2 性能优化效果验证

当我们对Chord进行性能优化后，需要验证优化是否真正有效：

# tests/performance/test_optimization_verification.py import unittest import time from unittest.mock import patch, MagicMock from chord_video_analyzer import VideoAnalyzer class TestChordOptimizationVerification(unittest.TestCase): def setUp(self): self.analyzer = VideoAnalyzer() def test_caching_mechanism_effectiveness(self): """测试缓存机制的有效性""" # 第一次分析（冷启动） start_time = time.time() with patch('chord_video_analyzer.VideoAnalyzer._load_video_metadata') as mock_meta: mock_meta.return_value = {'duration': 30.0, 'fps': 30.0} with patch('chord_video_analyzer.VideoAnalyzer._run_full_analysis') as mock_analyze: mock_analyze.return_value = MagicMock() self.analyzer.analyze("test.mp4", features=['temporal_reasoning']) first_duration = time.time() - start_time # 第二次分析（热启动，应利用缓存） start_time = time.time() with patch('chord_video_analyzer.VideoAnalyzer._load_video_metadata') as mock_meta: mock_meta.return_value = {'duration': 30.0, 'fps': 30.0} with patch('chord_video_analyzer.VideoAnalyzer._run_full_analysis') as mock_analyze: mock_analyze.return_value = MagicMock() self.analyzer.analyze("test.mp4", features=['temporal_reasoning']) second_duration = time.time() - start_time # 验证缓存使性能提升至少50% speedup_ratio = first_duration / second_duration if second_duration > 0 else 0 self.assertGreater(speedup_ratio, 1.5, f"Cache speedup ratio {speedup_ratio:.2f} < 1.5 expected") def test_batch_processing_efficiency(self): """测试批量处理效率""" # 模拟批量处理10个视频 video_paths = [f"video_{i}.mp4" for i in range(10)] start_time = time.time() with patch('chord_video_analyzer.VideoAnalyzer._load_video_metadata') as mock_meta: mock_meta.return_value = {'duration': 10.0, 'fps': 30.0} with patch('chord_video_analyzer.VideoAnalyzer._run_full_analysis') as mock_analyze: mock_analyze.return_value = MagicMock() # 批量分析 results = self.analyzer.batch_analyze( video_paths, features=['event_detection'], max_workers=4 ) batch_duration = time.time() - start_time # 单个视频分析时间 single_start = time.time() self.analyzer.analyze("single.mp4", features=['event_detection']) single_duration = time.time() - single_start # 验证批量处理效率（应接近线性加速） expected_batch_time = single_duration * len(video_paths) / 4 * 0.8 # 80%效率 self.assertLess(batch_duration, expected_batch_time * 1.2) def test_memory_usage_optimization(self): """测试内存使用优化""" # 模拟大视频分析（10分钟） with patch('chord_video_analyzer.VideoAnalyzer._load_video_metadata') as mock_meta: mock_meta.return_value = {'duration': 600.0, 'fps': 30.0} with patch('chord_video_analyzer.VideoAnalyzer._run_full_analysis') as mock_analyze: mock_analyze.return_value = MagicMock() # 监控内存使用（简化版） import psutil import os process = psutil.Process(os.getpid()) memory_before = process.memory_info().rss / 1024 / 1024 # MB self.analyzer.analyze("large.mp4", features=['spatial_tracking']) memory_after = process.memory_info().rss / 1024 / 1024 # MB memory_used = memory_after - memory_before # 验证内存使用在合理范围内（< 2GB） self.assertLess(memory_used, 2000, f"Memory usage {memory_used:.1f}MB exceeds 2000MB limit")

这些测试验证了Chord的关键性能特性：缓存机制是否有效、批量处理是否带来实际收益、内存使用是否可控。特别是内存使用测试，确保Chord在处理长视频时不会耗尽系统资源。

6. 实战应用：构建完整的测试工作流

单个测试用例只是起点，真正的价值在于将它们组织成一个可重复、可维护的完整测试工作流。在实际项目中，我建议采用分层测试策略，从快速反馈到全面验证。

6.1 分层测试策略

我们把测试分为三个层次：

单元测试层：验证单个函数或方法，执行速度快（毫秒级），作为CI/CD流水线的第一道关卡
集成测试层：验证多个组件协同工作，执行时间中等（秒级），确保各模块接口兼容
端到端测试层：验证完整业务流程，执行时间较长（分钟级），使用真实或模拟的视频数据

# tests/integration/test_integration_workflow.py import unittest import tempfile import os from chord_video_analyzer import VideoAnalyzer class TestChordIntegrationWorkflow(unittest.TestCase): def setUp(self): self.analyzer = VideoAnalyzer() def test_complete_analysis_workflow(self): """测试完整的分析工作流""" # 模拟一个典型的工作流：上传->预处理->分析->结果导出 with tempfile.TemporaryDirectory() as temp_dir: # 1. 创建模拟视频文件 video_path = os.path.join(temp_dir, "workflow_test.mp4") with open(video_path, 'wb') as f: f.write(b'mock video content') # 2. 执行完整分析流程 try: result = self.analyzer.analyze( video_path, features=['temporal_reasoning', 'spatial_tracking', 'event_detection'], confidence_threshold=0.5, output_format='json' ) # 3. 验证结果完整性 self.assertTrue(hasattr(result, 'temporal_events')) self.assertTrue(hasattr(result, 'spatial_tracks')) self.assertTrue(hasattr(result, 'detected_events')) self.assertTrue(hasattr(result, 'summary')) # 4. 验证结果导出 json_output = result.to_json() self.assertIn('"temporal_events"', json_output) self.assertIn('"spatial_tracks"', json_output) # 5. 验证摘要生成 summary = result.get_summary() self.assertIsInstance(summary, str) self.assertGreater(len(summary), 50) # 摘要应该有一定长度 except Exception as e: self.fail(f"Complete workflow failed with exception: {e}") def test_error_propagation_in_workflow(self): """测试工作流中的错误传播""" # 模拟在工作流中间步骤失败的情况 with patch('chord_video_analyzer.VideoAnalyzer._preprocess_video') as mock_preprocess: mock_preprocess.side_effect = RuntimeError("Preprocessing failed") with self.assertRaises(RuntimeError) as context: self.analyzer.analyze("test.mp4", features=['temporal_reasoning']) self.assertIn("Preprocessing failed", str(context.exception))

6.2 CI/CD集成配置

为了让测试真正发挥作用，我们需要将其集成到持续集成流程中。以下是一个.github/workflows/test.yml的示例配置：

name: Chord Video Analyzer Tests on: push: branches: [main, develop] pull_request: branches: [main, develop] jobs: test: runs-on: ubuntu-latest steps: - uses: actions/checkout@v3 - name: Set up Python uses: actions/setup-python@v3 with: python-version: '3.9' - name: Install dependencies run: | python -m pip install --upgrade pip pip install -r requirements.txt pip install pytest pytest-cov mock - name: Run unit tests run: pytest tests/unit/ -v --tb=short - name: Run integration tests run: pytest tests/integration/ -v --tb=short - name: Run performance tests (with timeout) run: pytest tests/performance/ -v --timeout=300 - name: Generate coverage report run: pytest --cov=chord_video_analyzer --cov-report=html --cov-report=term-missing - name: Upload coverage to Codecov uses: codecov/codecov-action@v3 with: token: ${{ secrets.CODECOV_TOKEN }}

这个CI配置确保每次代码变更都会自动运行三类测试，并生成覆盖率报告。特别值得注意的是性能测试设置了300秒超时，防止某个测试用例无限期挂起。

6.3 测试报告与质量门禁

最后，我们需要建立质量门禁，确保只有达到质量标准的代码才能合并：

# tests/utils/test_quality_gate.py import unittest import json from pathlib import Path class TestChordQualityGate(unittest.TestCase): def test_code_coverage_threshold(self): """测试代码覆盖率门禁""" # 读取coverage.json报告 try: with open('htmlcov/coverage.json', 'r') as f: coverage_data = json.load(f) total_coverage = coverage_data['totals']['percent_covered'] # 要求核心模块覆盖率不低于85% self.assertGreaterEqual( total_coverage, 85.0, f"Code coverage {total_coverage:.1f}% below required 85%" ) except FileNotFoundError: self.fail("Coverage report not found - run tests with --cov flag") def test_test_execution_success_rate(self): """测试执行成功率门禁""" # 在实际项目中，这会从CI日志中解析 # 这里简化为检查是否有失败的测试 # 在真实CI中，我们会统计最近10次构建的成功率 # 模拟：要求所有测试必须通过 pass # unittest框架本身会确保这一点 def test_performance_regression_prevention(self): """性能回归预防测试""" # 读取历史性能基准 baseline_file = Path('tests/performance/baseline.json') if baseline_file.exists(): with open(baseline_file, 'r') as f: baseline = json.load(f) # 检查当前性能是否比基线差超过10% current_perf = self._get_current_performance() for test_name, baseline_time in baseline.items(): if test_name in current_perf: regression_ratio = current_perf[test_name] / baseline_time self.assertLessEqual( regression_ratio, 1.1, f"Performance regression detected for {test_name}: " f"{regression_ratio:.2f}x slower than baseline" ) def _get_current_performance(self): """获取当前性能数据（简化版）""" return { 'small_video_analysis': 1500.0, 'medium_video_analysis': 6500.0, 'batch_processing': 12000.0 }

这个质量门禁测试确保了三个关键质量指标：代码覆盖率、测试执行成功率、性能回归控制。只有当所有门禁都通过时，代码才能进入生产环境。