Automated Identification of Security Requirements: A Machine Learning Approach

Armin Kobilica, Mohammed Ayub, Jameleddine Hassine

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

Early characterization of security requirements supports system designers to integrate security aspects into early architectural design. However, distinguishing security related requirements from other functional and non-functional requirements can be tedious and error prone. To address this issue, machine learning techniques have proven to be successful in the identification of security requirements. In this paper, we have conducted an empirical study to evaluate the performance of 22 supervised machine learning classification algorithms and two deep learning approaches, in classifying security requirements, using the publicly availble SecReq dataset. More specifically, we focused on the robustness of these techniques with respect to the overhead of the pre-processing step. Results show that Long short-term memory (LSTM) network achieved the best accuracy (84%) among non-supervised algorithms, while Boosted Ensemble achieved the highest accuracy (80%), among supervised algorithms.

Original languageEnglish
Title of host publicationProceedings of EASE 2020 - Evaluation and Assessment in Software Engineering
PublisherAssociation for Computing Machinery
Pages475-480
Number of pages6
ISBN (Electronic)9781450377317
DOIs
StatePublished - 15 Apr 2020

Publication series

NameACM International Conference Proceeding Series

Bibliographical note

Publisher Copyright:
© 2020 ACM.

Keywords

  • Fast Pre-processing Techniques
  • Machine Learning
  • Security Requirements

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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