TY - GEN
T1 - Classification of non-functional requirements using fuzzy similarity KNN Based on ISO / IEC 25010
AU - Raharja, Irit Maulana Sapta
AU - Siahaan, Daniel Oranova
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - In developing software, one of the important role is non-functional requirements. They are often forgotten by the developer, so that it causes adverse effects. For this reason, a non-functional requirement classification method is needed to make it easier for software developers to classify non-functional requirements from requirement document. Most of studies in this area are focusing on single label classification. However, a non-functional requirement sentence can be classified to more than one class. Therefore, a classification technique that support multi-labels was needed. This research proposes a model for classifying non-functional requirements into quality aspects based on ISO/IEe 25010. It uses Fuzzy Similarity KNN (FSKNN) for building the sentence-based classification model. Six difference dataset which is contain 1432 sentence is used in the test. Best accuracy, precision and recall value from classification using FSKNN respectively is 42.8%, 68.1%, and 55.9%.
AB - In developing software, one of the important role is non-functional requirements. They are often forgotten by the developer, so that it causes adverse effects. For this reason, a non-functional requirement classification method is needed to make it easier for software developers to classify non-functional requirements from requirement document. Most of studies in this area are focusing on single label classification. However, a non-functional requirement sentence can be classified to more than one class. Therefore, a classification technique that support multi-labels was needed. This research proposes a model for classifying non-functional requirements into quality aspects based on ISO/IEe 25010. It uses Fuzzy Similarity KNN (FSKNN) for building the sentence-based classification model. Six difference dataset which is contain 1432 sentence is used in the test. Best accuracy, precision and recall value from classification using FSKNN respectively is 42.8%, 68.1%, and 55.9%.
KW - Classification
KW - FSKNN
KW - ISO /IEC 25010
KW - Non-Functional Requirements
UR - http://www.scopus.com/inward/record.url?scp=85073502298&partnerID=8YFLogxK
U2 - 10.1109/ICTS.2019.8850944
DO - 10.1109/ICTS.2019.8850944
M3 - Conference contribution
AN - SCOPUS:85073502298
T3 - Proceedings of 2019 International Conference on Information and Communication Technology and Systems, ICTS 2019
SP - 264
EP - 269
BT - Proceedings of 2019 International Conference on Information and Communication Technology and Systems, ICTS 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th International Conference on Information and Communication Technology and Systems, ICTS 2019
Y2 - 18 July 2019
ER -