Classification of Non-Functional Requirements Using Semantic-FSKNN Based ISO/IEC 9126

Ramadhani, Denni Aldi; Rochimah, Siti; Yuhana, Umi Laili

doi:10.12928/telkomnika.v13i4.2300

Cited by 15 publications

(9 citation statements)

References 16 publications

(19 reference statements)

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“…EBUTUHAN non-fungsional yang berkaitan erat dengan aspek kualitas perangkat lunak memiliki peran yang sangat besar untuk mendukung kesuksesan pengembangan perangkat lunak [1] [2]. Non-fungsional merupakan kendala pada layanan atau fungsi yang ditawarkan oleh sistem secara keseluruhan seperti usability, reliability, dan security [3].…”

Section: Pendahuluanunclassified

“…Kerancuan akan mempersulit analis dalam mengidentifikasi aspek kualitas kebutuhan non-fungsional yang terdapat di dalamnya [7]. Oleh karena itu dibutuhkan suatu cara untuk dapat mengidentifikasi aspek kualitas kebutuhan non-fungsional, salah satunya dengan cara melakukan klasifikasi dari kalimat-kalimat kebutuhan yang tertulis dalam dokumen kebutuhan tersebut [2].…”

Section: Pendahuluanunclassified

“…Faktanya faktor semantik dapat meningkatkan hasil klasifikasi menjadi lebih baik [11]. Metode keempat berusaha menambahkan pengukuran semantik ke dalam proses algoritma FSKNN [2]. Meskipun standar kualitas ISO/IEC 9126 telah digantikan oleh ISO/IEC 25010 pada tahun 2011.…”

Section: Pendahuluanunclassified

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Klasifikasi Kebutuhan Non-Fungsional Menggunakan FSKNN Berbasis Iso/Iec 25010

Hakim

Rochimah

Fatichah

2019

JUTI

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3) ABSTRAK Aspek kualitas kebutuhan non-fungsional memiliki peran yang sangat besar dalam kesuksesan pengembangan perangkat lunak. Namun, mengidentifikasi aspek kualitas kebutuhan non-fungsional merupakan hal yang sulit untuk dilakukan. Karena aspek kualitas kebutuhan non-fungsional tersebut ditemukan tidak lengkap, tersembunyi, dan tercampur di dalam kalimatkalimat kebutuhan lain pada dokumen. Penelitian yang ada mampu mengidentifikasi aspek kebutuhan non-fungsional dengan melakukan klasifikasi. Meskipun standar kualitas ISO/IEC 9126 telah digantikan oleh ISO/IEC 25010 pada tahun 2011. Namun, pada proses pelabelan masih menggunakan standar kualitas ISO / IEC 9126. Oleh karena itu, standar kualitas yang digunakan untuk melabeli kalimat kebutuhan pada penelitian ini adalah ISO/IEC 25010. Sedangkan metode klasifikasi yang digunakan adalah Fuzzy Similarity based K-Nearest Neighbor (FSKNN). Metode klasifikasi yang digunakan diuji dengan menggunakan nilai tetangga terdekat 10, 20 dan 30. Pada penelitian ini metode FSKNN berhasil memeroleh nilai tertinggi berdasarkan ground truth pakar yaitu accuracy sebesar 98.15%, precision sebesar 6.15 dan recall 21.73.

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Klasifikasi Kebutuhan Non-Fungsional Menggunakan FSKNN Berbasis Iso/Iec 25010

Hakim

Rochimah

Fatichah

2019

JUTI

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“…Multiple types of classifiers are used in the paper and it is resulted that k-NN classifier achieve the maximum result in identifying non-functional requirements. Ramadhani et al (2015) proposed an automated system for the identification of NFRs taking account of an algorithm FSKNN (Fuzzy similarity based Knearest neighbor) a requirement sentences-based classification algorithm. In FSKNN algorithm semantic factors and semantic relatedness measurement are not considered.…”

Section: Literature Reviewmentioning

confidence: 99%

A review on automatic extraction and classification of non-functional requirements

Khan¹,

Khan

2017

Int. j. adv. appl. sci

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Extraction and classification of NFRs plays a vital role in the software development process. NFRs are often misunderstood and ignored due to which certain important aspect of the software such as robustness availability usability performance and security are compromised. Several different extraction and classification techniques proposed in the literature. In this study, we have made an effort to critically analyze different NFRs extraction and classification techniques in order to see their merits and demerits, as well as the scenarios in which these techniques can be helpful. Specifically we have identified the limitations of these techniques, their significance and the possible improvements that can be made to make these techniques more useful. In this paper, we have critically evaluated various techniques and methods proposed in the literature on the automatic extraction and classification of NFRs. The findings of the existing techniques are provided in the paper. The critical analysis tables are provided in this study to highlight the significance of NFRs extraction and classification technique.

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“…As a result, measuring the links between candidate features, selected features, and categories in the selection process is difficult. Classifier-independent Filter, classifierdependent Wrapper, and Embedded techniques are three types of supervised algorithms that deal with diverse interactions between features and classifiers [10]. The explanation of various techniques is given below:…”

Section: Introductionmentioning

confidence: 99%

Hybrid Feature Selection based Classifier for Non Functional Requirements

Handa¹

2022

MSEA

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Feature Selection aims at finding the most relevant features of a problem domain. It is very helpful in improving computational speed and prediction accuracy. However, identification of useful features from hundreds or even thousands of related features is a nontrivial task. In this paper, we introduce a hybrid FS method which combines two FS methods – the filters and the wrappers for NFRs classification. Candidate features are first selected from the original feature set through two filter techniques then the union of these two sets are further refined by more accurate wrappers. This hybrid mechanism takes advantage of both the filters and the wrappers. The mechanism is examined by primary dataset based on NFRs. Experimental results show that better prediction accuracy can be achieved with a smaller feature set.

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Classification of Non-Functional Requirements Using Semantic-FSKNN Based ISO/IEC 9126

Abstract: Abstract

Cited by 15 publications

References 16 publications

Klasifikasi Kebutuhan Non-Fungsional Menggunakan FSKNN Berbasis Iso/Iec 25010

Klasifikasi Kebutuhan Non-Fungsional Menggunakan FSKNN Berbasis Iso/Iec 25010

A review on automatic extraction and classification of non-functional requirements

Hybrid Feature Selection based Classifier for Non Functional Requirements

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