Text Categorization Approach for Secure Design Pattern Selection Using Software Requirement Specification
Author(s) -
Ishfaq Ali,
Muhammad Asif,
Muhammad Shahbaz,
Adnan Khalid,
Mariam Rehman,
Aziz Guergachi
Publication year - 2018
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2018.2883077
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Secure patterns provide a solution for the security requirement of the software. There are large number of secure patterns, and it is quite difficult to choose an appropriate pattern. Moreover, selection of these patterns needs security knowledge; generally, developers are not specialized in the domain of security knowledge. This paper can help in the selection of secure pattern on the basis of tradeoffs of the secure pattern using text categorization. A repository of secure design patterns is used as a data set and a repository of requirements artifacts in the form of software requirements specification (SRS) are used for this paper. A text categorization scheme, which begins with preprocessing, indexing of secure patterns, ends up by querying SRS features for retrieving secure design pattern using document retrieval model. For the evaluation of the proposed model, we have used three different domains’ SRS. These three SRS documents represent three different domains, i.e., e-commerce, social media, and desktop utility program. A traditional precision and recall method along with F-measure used for evaluation of information/document retrieval model is used to evaluate the results. F-measure for 17 different design problems shows around 81% accuracy with recall up to 0.69%.
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