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.
|Title of host publication
|Proceedings of EASE 2020 - Evaluation and Assessment in Software Engineering
|Association for Computing Machinery
|Number of pages
|Published - 15 Apr 2020
|ACM International Conference Proceeding Series
Bibliographical notePublisher Copyright:
© 2020 ACM.
- Fast Pre-processing Techniques
- Machine Learning
- Security Requirements
ASJC Scopus subject areas
- Human-Computer Interaction
- Computer Vision and Pattern Recognition
- Computer Networks and Communications