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Summary
This summary is machine-generated.

This study explores patterns in software development using machine learning for intelligent systems. It aims to enhance software security and requirements management by modeling source code and identifying vulnerabilities.

Keywords:
Bidirection encoders transformersknowledge graphsrequirements managementsoftware developmenttransfer learning

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Area of Science:

  • Computer Science
  • Software Engineering
  • Artificial Intelligence

Background:

  • Source code contains valuable knowledge for intelligent software systems and reuse.
  • Software development faces challenges in security and requirements management.
  • Machine learning (ML) offers potential solutions for these challenges.

Purpose of the Study:

  • To explore patterns in software development processes and artifacts.
  • To contribute to a smart requirements management system focused on vulnerability security.
  • To investigate ML applications in software development and source code analysis.

Main Methods:

  • Reviewing existing work on source code modeling and vulnerability detection.
  • Exploring program representation principles for better understanding.
  • Investigating deeper source code modeling possibilities.
  • Examining ML best practices in the context of source code modeling.

Main Results:

  • Identified patterns in software development artifacts.
  • Reviewed approaches for source code modeling and vulnerability analysis.
  • Explored the application of ML in software engineering.

Conclusions:

  • Source code modeling and ML are crucial for intelligent software systems.
  • This work lays the foundation for a secure requirements management system.
  • Further research into ML-driven source code analysis is warranted.