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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.

Cluster Computing
|April 21, 2021
PubMed
Summary
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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.

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:

Keywords:
ClassificationFeature optimizationPCAPROMISE datasetSVMSoftware defects detection

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  • 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.