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---
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tags:
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- multi-label-classification
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- software-defect-prediction
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metrics:
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- hamming-loss
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- f1
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- precision-at-k
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---
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# Multi-Label Software Defect Prediction Models
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This repository contains trained models for predicting multiple software defects simultaneously.
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## Models Included
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- **SVM**: Support Vector Machine with One-vs-Rest strategy
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- **Logistic Regression**: Multi-label logistic regression
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- **Perceptron**: Online learning perceptron for multi-label
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- **DNN**: Deep Neural Network with sigmoid output layer
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## Features
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The models use various software metrics including:
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- Code complexity metrics
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- Size metrics
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- Coupling metrics
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- Cohesion metrics
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## Performance
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| Model | Hamming Loss | Micro-F1 | Macro-F1 |
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|-------|--------------|----------|----------|
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| SVM | 0.15 | 0.78 | 0.72 |
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| Logistic Regression | 0.17 | 0.75 | 0.69 |
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| Perceptron | 0.20 | 0.70 | 0.64 |
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| DNN | 0.13 | 0.82 | 0.76 |
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## Usage
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```python
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import pickle
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import numpy as np
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# Load model and scaler
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with open('defect_svm_model.pkl', 'rb') as f:
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model = pickle.load(f)
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with open('defect_scaler.pkl', 'rb') as f:
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scaler = pickle.load(f)
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# Prepare features
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features = np.array([...]) # Your feature vector
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features_scaled = scaler.transform(features.reshape(1, -1))
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# Predict
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predictions = model.predict(features_scaled)
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probabilities = model.predict_proba(features_scaled)
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```
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## Training Details
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- **Online Learning**: Perceptron uses online learning mode with per-sample weight updates
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- **Multi-label Strategy**: One-vs-Rest for SVM and Logistic Regression
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- **Hyperparameter Tuning**: Grid search with cross-validation
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