52 lines
1.7 KiB
Markdown
52 lines
1.7 KiB
Markdown
---
|
|
title: "Defect Prediction"
|
|
type: concept
|
|
tags: [testing, machine-learning, quality]
|
|
sources: [testing-test-results-analyzer]
|
|
last_updated: 2026-04-28
|
|
---
|
|
|
|
## Definition
|
|
|
|
缺陷预测——使用机器学习模型基于代码指标和历史缺陷数据，预测哪些代码区域最可能包含缺陷，指导测试资源的定向投入。
|
|
|
|
## Approach
|
|
|
|
### Feature Engineering (from TestResultsAnalyzer)
|
|
|
|
```python
|
|
from sklearn.ensemble import RandomForestClassifier
|
|
from sklearn.model_selection import train_test_split
|
|
|
|
# 特征：代码指标
|
|
features = extract_code_metrics() # 圈复杂度、代码行数、变更频率等
|
|
historical_defects = load_historical_defect_data() # 历史缺陷标签
|
|
|
|
# 训练/测试分割
|
|
X_train, X_test, y_train, y_test = train_test_split(
|
|
features, historical_defects, test_size=0.2, random_state=42
|
|
)
|
|
|
|
# Random Forest 分类器
|
|
model = RandomForestClassifier(n_estimators=100, random_state=42)
|
|
model.fit(X_train, y_train)
|
|
|
|
# 预测 + 置信度 + 特征重要性
|
|
predictions = model.predict_proba(features)
|
|
feature_importance = model.feature_importances_
|
|
accuracy = model.score(X_test, y_test)
|
|
```
|
|
|
|
### Key Metrics
|
|
|
|
- **Prediction Accuracy**：模型在测试集上的准确率，目标 ≥ 85%。
|
|
- **Feature Importance**：哪些代码指标（圈复杂度、变更频率、代码行数等）对缺陷预测最有预测力。
|
|
- **Confidence Score**：每个预测结果附带置信度评分。
|
|
|
|
## Connections
|
|
|
|
- [[Statistical-Analysis]]：模型验证需统计显著性检验。
|
|
- [[Test-Coverage-Analysis]]：预测的高风险区域优先增加测试覆盖率。
|
|
- [[Release-Readiness-Assessment]]：缺陷预测结果纳入整体发布就绪度评估。
|
|
- [[Quality-Metrics]]：缺陷密度是预测模型的目标变量。
|