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Cross-Project Defect Prediction Based on Two-Phase Feature Importance Amplification.

Ying Xing¹, Wanting Lin¹, Xueyan Lin¹

¹School of Artificial Intelligence, Beijing University of Posts and Telecommunications, 100876 Beijing, China.

Computational Intelligence and Neuroscience

|April 28, 2022

Summary

This summary is machine-generated.

This study introduces a two-phase feature importance amplification (TFIA) model to improve cross-project defect prediction (CPDP). TFIA effectively addresses data distribution differences and class imbalance, enhancing defect prediction accuracy in software engineering.

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Area of Science:

Software Engineering
Computational Intelligence
Machine Learning

Background:

Cross-project defect prediction (CPDP) leverages data from source projects to predict defects in target projects.
CPDP faces challenges due to class imbalance and differing data distributions across projects.
Accurate defect prediction is crucial for efficient software testing and quality assurance.

Purpose of the Study:

To propose a novel Two-Phase Feature Importance Amplification (TFIA) model for CPDP.
To address the key challenges of class imbalance and data distribution heterogeneity in CPDP.
To enhance the accuracy and efficiency of defect prediction models in software engineering.

Main Methods:

A two-phase approach involving domain adaptation and classification.
Domain adaptation phase: Reduces data distribution differences via project filtering and correlation-based feature selection (greedy best-first search).
Classification phase: Employs Random Forest to amplify feature importance and build a sensitive prediction model.

Main Results:

The TFIA model demonstrated significant improvements in CPDP performance.
Ablation and comparison experiments on the AEEEM database validated the model's effectiveness.
The TFIA CPDP model exhibited stable and efficient performance across experiments.

Conclusions:

The proposed TFIA model effectively mitigates challenges in CPDP.
TFIA offers a robust solution for improving defect prediction accuracy.
The model's stability and efficiency provide a strong basis for practical application in software engineering.