Software Engineering & Data Mining
Feature Disentanglement-Based Heterogeneous Defect Prediction
This research introduces FD-HDP, a novel method for Heterogeneous Defect Prediction (HDP) that disentangles domain-related and domain-independent features. By doing so, it improves prediction performance and model interpretability in cross-project defect prediction, especially in scenarios with limited labeled data. Experiments on four public datasets demonstrate significant advantages over existing WPDP and HDP methods across multiple key metrics, making it a promising approach for enhancing software quality.
Key Performance Indicators
Our analysis shows significant improvements across key metrics after implementing this AI solution.
+18.64%
F-Measure Improvement
+24.75%
AUC Improvement
+46.99%
G-Mean Improvement
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Spotlight
Flowchart
Comparison
Case Study
+46.99%
G-Mean Improvement over baselines, indicating superior handling of class imbalance.
Enterprise Process Flow
Input Layer (MLP for common latent space)
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Disentanglement Layer (Domain-related & Shared Extractors)
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Reconstruction (Original features retained)
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Domain Adversarial Loss (Ensures domain-independence)
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Prediction Layer (Domain-related & Shared Predictors)
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Weighted Sum (Final Defect Probability)
FD-HDP vs. Traditional Methods
FD-HDP significantly outperforms existing WPDP and HDP methods by effectively disentangling features and transferring domain-independent knowledge.
| Feature | FD-HDP Advantage | Traditional Methods (e.g., SMOTUNED, DSSDPP) |
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| Feature Disentanglement |
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| Knowledge Transfer |
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| Class Imbalance Handling |
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| Prediction Performance |
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Enterprise Application: Cross-language Defect Prediction
Company X, with existing Python-based data analytics software, acquired a new Java-based ERP system. Traditional IDP methods were insufficient due to language and architectural heterogeneity. FD-HDP was applied to leverage Python defect data for Java project prediction. The method involved: 1. Data Collection & Labeling (extracting historical defects, manual labeling of new Java code); 2. Implementation & Training (standardizing features, SMOTE for imbalance, training domain-independent/related extractors with adversarial loss, fine-tuning); 3. Key Considerations (manual labeling costs, computational resources, feature generalization). This enabled efficient defect prediction, reducing quality assurance costs for new projects despite significant language differences.
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Estimated Annual Savings
$0
Annual Hours Reclaimed
0
Strategic Implementation Timeline
Our phased approach ensures a smooth transition and rapid value realization.
Phase 1: Data Acquisition & Preprocessing
Gather existing defect data from source projects, and a small, labeled sample from the target project. Apply min-max standardization and SMOTE for class imbalance.
Phase 2: Model Pre-training & Disentanglement
Train the input and disentanglement layers using MLP and the feature disentanglement network. Focus on reconstructing original features and maximizing domain adversarial loss for domain-independent features.
Phase 3: Model Fine-tuning & Prediction
Integrate the prediction layer. Fine-tune the entire model with labeled data. Obtain final defect probabilities by weighting domain-related and domain-independent predictors.
Phase 4: Validation & Deployment
Evaluate model performance using established metrics. Implement the solution incrementally in real-world enterprise environments, starting with small codebases.
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