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Extraction of use case diagram elements using natural language processing and network science.

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This summary is machine-generated.

This study introduces an automated method for extracting software engineering use cases from natural language requirements. The approach significantly improves accuracy, reducing manual effort in software design.

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Area of Science:

  • Software Engineering
  • Natural Language Processing
  • Artificial Intelligence

Background:

  • Automated extraction of software engineering artifacts, particularly use cases, from natural language requirements remains a significant challenge.
  • Existing methods often require human intervention, specific formalisms, or restricted language, limiting their practical application.
  • Use cases are crucial artifacts in software design and development, guiding system functionality and user interaction.

Purpose of the Study:

  • To propose and validate a fully automated approach for extracting use cases, actors, and their relationships from natural language requirements.
  • To overcome the limitations of existing semi-automated or formalism-dependent methods.
  • To reduce human effort and enhance efficiency in the software development lifecycle.

Main Methods:

  • Utilized Natural Language Processing (NLP) techniques to analyze textual requirements.
  • Employed Network Science principles to model and extract relationships between identified use case elements.
  • Developed an automated pipeline requiring no human intervention or specialized formalisms.

Main Results:

  • Achieved an average accuracy of approximately 71.5% (F-Measure) in extracting use case elements.
  • Demonstrated a significant improvement over a baseline method that achieved an average of 16% accuracy (F-Measure).
  • Validated the approach through several literature-based case studies, confirming its effectiveness.

Conclusions:

  • The proposed automated approach successfully extracts use case elements from natural language requirements with high accuracy.
  • This method offers a viable solution for reducing manual effort and increasing efficiency in software design and development.
  • The integration of NLP and Network Science provides a robust framework for automated software artifact extraction.