Classification of Non-Functional Requirements Using Semantic-FSKNN Based ISO/IEC 9126
Non-functional requirements is one of the important factors that play a role in the success of software development that is often overlooked by developers, so it cause adverse effects. In order to obtain the non-functional requirements, it requires an identification automation system of non-functional requirements. This research proposes an automation system of identification of non-functional requirements from the requirement sentence-based classification algorithms of FSKNN with the addition of semantic factors such as the term development by hipernim and measurement of semantic relatedness between the term and every category of quality aspects based ISO / IEC 9126. In the test, the dataset is 1342 sentences from six different datasets. The result of this research is that the Semantic-FSKNN method can reduce the value of hamming loss or error rate by 21.9%, and also raise the value of accuracy by 43.7%, and also the precision value amounted to 73.9% compared to FSKNN method without the addition of semantic factors in it.
S. Ullah, M. Iqbal and A. M. Khan. A Survey on Issues in Non-Functional Requirements Elicitation. 2011 International Conference on Computer Networks and Information Technology (ICCNIT). 2011; Peshawar, Pakistan.
A. Finkelstein and J. Dowel. A Comedy of Errors: the London Ambulance Service case study. Proceedings of the 8th International Workshop on Software Specification and Design.1996.
J. Bertman and N. Skolnik. EHRs Get a Failing Grade on Usability. Internal Medicine News. 2010; 43: 50.
C. Hoskinson, "Politico," News - http://www.politico.com/news/stories/0611/58051.html.
J. Slankas and L. Williams, Automated Extraction of Non-Functional Requirements in Available Documentation. 2013 1st International Workshop on Natural Language Analysis in Software Engineering (NaturaLiSE). San Francisco, CA, USA. 2013.
A. Casamayor, D. Godoy and M. Campo. Identification of non-functional requirements in textual specifications : A semi-supervised learning approach. Information and Software Technology. 2010; 52: 436-445.
Y. Ko, S. Park, J. Seo and S. Choi. Using classification techniques for informal requirements in the requirements analysis-supporting system. Information and Software Technology. 2007; 49: 1128-1140.
W. Suharso and S. Rochimah. Sistem Deteksi dan Klasifikasi Otomatis Kebutuhan Non Fungsional Berbasis ISO/IEC 9126 in Surabaya. Master Thesis. Surabaya : Postgraduate ITS 2013.
J.-Y. Jiang, S.-C. Tsai and S.-J. Lee. FSKNN: Multi-label text categorization based on fuzzy similarity and k nearest neighbors. Expert Systems with Applications. 2012; 39:2813-2821.
M. Choudhari. Extending The Hirst And St-onge Measure of Semantic Relatedness for the Unified Medical Language System. Master Thesis. Minnesota: University of Minnesota, 2012.
G. Hirst and D. St-Onge. Lexical chains as representations of context for the detection and correction of malapropisms. Christiane Fellbaum edision, MIT Press. 1998; 305-332.
PROMISE, tera-PROMISE Repository. - http://openscience.us/repo/requirements/.
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