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Within-project and cross-project defect prediction based on model averaging.

Tong Li¹, Zhong Wang², Peibei Shi¹

¹School of Computer and Artificial Intelligence, Hefei Normal University, No. 1688 Jinxiu Avenue, Hefei, 230601, Anhui, China.

Scientific Reports

|February 21, 2025

Summary

This summary is machine-generated.

This study introduces a model averaging technique for software defect prediction. This method improves accuracy in both within-project and cross-project defect detection compared to existing algorithms.

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Area of Science:

Computer Science
Software Engineering

Background:

Software defect prediction is crucial for economic and financial sectors.
Early identification of defective software modules is highly significant.

Purpose of the Study:

To propose a novel within-project and cross-project software defect prediction technology.
To enhance prediction performance using model averaging theory.

Main Methods:

Utilized XGBoost and LightGBM as candidate machine learning models.
Applied model averaging by determining weights to minimize prediction errors.
Evaluated performance using cross-validation on four public datasets (NASA, AEEEM, ReLink, SoftLab).

Main Results:

Model averaging showed slightly improved results over XGBoost and LightGBM for within-project prediction.
Outperformed seven traditional machine learning algorithms in most within-project scenarios.
Demonstrated overall superior performance compared to four benchmark methods in cross-project prediction.

Conclusions:

The proposed model averaging method achieves robust and accurate software defect prediction.
This approach is effective for both within-project and cross-project defect prediction tasks.