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Principal component based support vector machine (PC-SVM): a hybrid technique for software defect detection.

Mohd Mustaqeem¹, Mohd Saqib²

¹CSE Department, Institute of Technology & Management (A.K.T.U), Aligarh, U.P India.

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|April 21, 2021

Summary

This summary is machine-generated.

This study introduces a hybrid machine learning model combining Principal Component Analysis (PCA) and Support Vector Machines (SVM) for accurate software defect prediction, significantly reducing complexity and improving performance.

Keywords:

Classification Feature optimization PCA PROMISE dataset SVM Software defects detection

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Area of Science:

Computer Science
Software Engineering
Machine Learning

Background:

Software defect prediction is crucial but challenging, with existing methods often suffering from high dimensionality and complexity.
Previous approaches without feature reduction face the curse of dimensionality, impacting accuracy and efficiency.
There is a need for advanced algorithms that enhance prediction accuracy while minimizing time and space complexities.

Purpose of the Study:

To develop a hybrid machine learning approach for more accurate and efficient software defect prediction.
To address the limitations of existing methods, particularly the curse of dimensionality and computational complexity.
To improve the accuracy and reduce the complexity of software defect prediction models.

Main Methods:

A hybrid machine learning model was developed by combining Principal Component Analysis (PCA) for feature optimization and Support Vector Machines (SVM) for classification.
The PROMISE dataset (CM1 and KC1) from NASA was utilized, split into training and testing sets.
Principal Component Analysis (PCA) was employed for feature reduction to decrease time complexity, followed by SVM classification. Hyperparameter tuning was performed using GridSearchCV.

Main Results:

The proposed hybrid PCA-SVM model achieved high accuracy rates: 95.2% for CM1 and 86.6% for KC1.
The model demonstrated superior performance compared to other existing methods across various evaluation criteria.
Feature optimization using PCA effectively reduced time complexity.

Conclusions:

The hybrid PCA-SVM model offers a significant improvement in software defect prediction accuracy and efficiency.
This approach effectively mitigates the curse of dimensionality and reduces computational overhead.
A limitation of SVM is its lack of probabilistic explanation; future research could explore methods to incorporate probabilistic margins for classification.