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Examining the Capacity of Text Mining and Software Metrics in Vulnerability Prediction.

Ilias Kalouptsoglou1,2, Miltiadis Siavvas1, Dionysios Kehagias1

  • 1Centre for Research and Technology Hellas, 57001 Thessaloniki, Greece.

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Summary

Deep learning models for software vulnerability prediction show text mining approaches outperform traditional software metrics. Combining both methods did not improve prediction accuracy.

Keywords:
dataset extensiondeep learningensemble learningmachine learningsoftware metricstext miningvulnerability prediction

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Area of Science:

  • Computer Science
  • Software Engineering
  • Cybersecurity

Background:

  • Software security is critical for delivering high-quality software.
  • Early detection of software vulnerabilities is essential for mitigation.
  • Vulnerability prediction models aid in identifying flaws during development.

Purpose of the Study:

  • To compare the effectiveness of text mining and software metrics for vulnerability prediction.
  • To investigate if combining text mining and software metrics enhances prediction accuracy.
  • To evaluate deep learning models for software vulnerability prediction.

Main Methods:

  • Developed and compared deep learning models using text mining techniques.
  • Developed and compared deep learning models using statically extracted software metrics.
  • Constructed hybrid models combining text mining and software metrics.
  • Utilized and extended a real-world vulnerability dataset.

Main Results:

  • Text mining-based models achieved a higher F2-score than software metrics-based models.
  • Enriching text mining models with software metrics did not improve predictive performance.
  • Deep learning models demonstrated varying effectiveness based on the input features.

Conclusions:

  • Text mining is a more effective approach for software vulnerability prediction compared to software metrics alone.
  • Hybrid models do not offer significant advantages over pure text mining models.
  • Further research can explore advanced feature engineering for improved vulnerability prediction.