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f233053/
software-defect-prediction

multi-label-classification

software-defect-prediction

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Multi-Label Software Defect Prediction Models

This repository contains trained models for predicting multiple software defects simultaneously.

Models Included

SVM: Support Vector Machine with One-vs-Rest strategy
Logistic Regression: Multi-label logistic regression
Perceptron: Online learning perceptron for multi-label
DNN: Deep Neural Network with sigmoid output layer

Features

The models use various software metrics including:

Code complexity metrics
Size metrics
Coupling metrics
Cohesion metrics

Performance

Model	Hamming Loss	Micro-F1	Macro-F1
SVM	0.15	0.78	0.72
Logistic Regression	0.17	0.75	0.69
Perceptron	0.20	0.70	0.64
DNN	0.13	0.82	0.76

Usage

python

import pickle
import numpy as np

# Load model and scaler
with open('defect_svm_model.pkl', 'rb') as f:
    model = pickle.load(f)
with open('defect_scaler.pkl', 'rb') as f:
    scaler = pickle.load(f)

# Prepare features
features = np.array([...])  # Your feature vector
features_scaled = scaler.transform(features.reshape(1, -1))

# Predict
predictions = model.predict(features_scaled)
probabilities = model.predict_proba(features_scaled)

Training Details

Online Learning: Perceptron uses online learning mode with per-sample weight updates
Multi-label Strategy: One-vs-Rest for SVM and Logistic Regression
Hyperparameter Tuning: Grid search with cross-validation

f233053/software-defect-prediction