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Enhancing software defect prediction: a framework with improved feature selection and ensemble machine learning.

Misbah Ali¹, Tehseen Mazhar¹, Amal Al-Rasheed²

¹Department of Computer Science & Information Technology, Virtual University of Pakistan, Lahore, Pakistan.

Peerj. Computer Science

|December 13, 2024

Summary

This summary is machine-generated.

This study introduces a five-stage framework for software defect prediction, enhancing accuracy to 95.1%. The novel approach improves efficiency by significantly reducing execution times for better software quality assurance.

Keywords:

AI Deep learning Ensemble Features selections Machine learning NB Quality RN SVM Software defect predication

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Area of Science:

Software Engineering
Machine Learning
Data Science

Background:

Effective software defect prediction is vital for quality assurance.
Current methods face challenges in accuracy and efficiency.
Early identification of defective modules improves the software development lifecycle.

Purpose of the Study:

To propose a comprehensive five-stage framework for software defect prediction.
To enhance the accuracy and efficiency of identifying defective software modules.
To address existing challenges in the field of software defect prediction.

Main Methods:

Utilized cleaned NASA defect datasets (CM1, JM1, MC2, MW1, PC1, PC3, PC4).
Applied genetic algorithm for optimal feature selection.
Employed ensemble machine learning with random forest, support vector machine, and naïve Bayes as base classifiers, combined with a voting master classifier.

Main Results:

Achieved a maximum accuracy of 95.1% in software defect prediction.
Demonstrated superior performance compared to state-of-the-art ensemble and base classifiers.
Significantly reduced training and testing execution times by an average of 51.52% and 52.31%, respectively.

Conclusions:

The proposed five-stage framework is highly effective for accurate software defect prediction.
The framework offers a computationally economical solution with enhanced efficiency.
The study contributes a robust and optimized approach to software quality assurance.