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Predicting defects in imbalanced data using resampling methods: an empirical investigation.

Ruchika Malhotra1, Juhi Jain2

  • 1Department of Software Engineering, Delhi Technological University (former Delhi College of Engineering), Shahbad Daulatpur, Delhi, India.

Peerj. Computer Science
|May 31, 2022
PubMed
Summary
This summary is machine-generated.

Software defect prediction (SDP) models struggle with imbalanced data. This study found that random oversampling significantly improves model accuracy, offering a guideline for effective defect prediction.

Keywords:
Class imbalance problemMachine learningResampling methodsSoftware defect predictionStatistical validation

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Area of Science:

  • Software Engineering
  • Machine Learning
  • Data Science

Background:

  • Class imbalance in object-oriented software projects leads to inaccurate defect prediction models.
  • High dimensionality from numerous software metrics further degrades model performance.

Purpose of the Study:

  • To identify influential software metrics for defect prediction using correlation feature selection.
  • To compare the effectiveness of 10 resampling methods for imbalanced data in software defect prediction.
  • To integrate stable performance evaluators and statistical validation for robust analysis.

Main Methods:

  • Correlation feature selection was employed to identify useful software metrics.
  • 10 resampling techniques were extensively analyzed on 12 Apache object-oriented datasets.
  • 15 machine learning techniques were applied, with performance evaluated using AUC, GMean, Balance, and sensitivity.

Main Results:

  • Statistical validation confirmed that resampling methods enhance software defect prediction (SDP) model performance.
  • Random oversampling demonstrated the best predictive capability among the evaluated methods.
  • Oversampling techniques generally outperformed undersampling methods in improving defect prediction.

Conclusions:

  • Resampling methods are crucial for improving the accuracy of software defect prediction models, especially with imbalanced datasets.
  • Random oversampling is a highly effective strategy for enhancing predictive performance in SDP.
  • The study provides a valuable guideline for metric selection and resampling method application in software defect prediction.