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Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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Software Systems Security Vulnerabilities Management by Exploring the Capabilities of Language Models Using NLP.

Raghavendra Rao Althar1,2, Debabrata Samanta3, Manjit Kaur4

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Summary
This summary is machine-generated.

This study introduces a knowledge management system using data science and language modeling to enhance software security. The developed deep learning model effectively identifies security vulnerabilities during software development, aiding engineers in building more secure systems.

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Area of Science:

  • Software Engineering
  • Data Science
  • Cybersecurity
  • Knowledge Management

Background:

  • Software system security is a critical concern for development teams.
  • Existing methods for ensuring software security often face practical implementation challenges.
  • A robust knowledge management system is needed to support secure software development.

Purpose of the Study:

  • To explore data science methods for building a knowledge management system for software security.
  • To investigate the role of language modeling in assessing software security.
  • To develop and evaluate a deep learning model for identifying security vulnerabilities in source code.

Main Methods:

  • Utilized data science techniques on insurance domain software development data.
  • Applied language modeling to source code for security analysis.
  • Developed a deep software security analysis model based on source code modeling.
  • Conducted extensive experiments to validate the model's performance.

Main Results:

  • The proposed knowledge management system effectively assists software development teams in ensuring system security.
  • The deep software security analysis model accurately classifies security vulnerabilities.
  • Language modeling capabilities were successfully leveraged for software security assessment.

Conclusions:

  • Data science and language modeling offer powerful tools for enhancing software security.
  • The developed model provides practical support for identifying vulnerabilities during development.
  • This approach contributes to building more secure software systems through proactive analysis.