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Identifying effective software vulnerability predictors is crucial for efficient security testing. This study pinpoints key features from SonarQube and CCCC tools to enhance machine learning models for predicting software vulnerabilities.

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Area of Science:

  • Computer Science
  • Software Engineering
  • Cybersecurity

Background:

  • Software vulnerabilities pose significant risks, including security breaches and financial losses.
  • Rapid software development cycles and limited resources necessitate efficient vulnerability prediction methods.
  • Existing approaches often lack a focused evaluation of relevant vulnerability features for machine learning models.

Purpose of the Study:

  • To identify and evaluate features from SonarQube and CCCC tools for software vulnerability prediction.
  • To determine the most effective features for training machine learning algorithms to predict software vulnerabilities.
  • To develop efficient vulnerability predictors by selecting optimal feature sets.

Main Methods:

  • Examined thirty-three features generated by SonarQube and CCCC static code analysis tools.
  • Trained thirteen distinct machine learning algorithms using selected features.
  • Employed correlation analysis and four feature selection techniques for comprehensive evaluation.
  • Utilized a large, publicly available dataset for experimental validation.

Main Results:

  • Identified small, highly efficient sets of features crucial for accurate software vulnerability prediction.
  • Demonstrated the effectiveness of selected features in training machine learning models.
  • Provided a validated methodology for feature selection in vulnerability prediction.

Conclusions:

  • The study successfully identified key features that significantly improve the efficiency and accuracy of software vulnerability prediction.
  • The findings enable targeted security testing by highlighting critical system components.
  • This research contributes to enhancing overall software security through data-driven feature selection.