Melola Discovery

← Melola Discovery

Akses terbuka Jurnal SINTA 1

An enhanced support vector regression model for agile projects cost estimation

IAES International Journal of Artificial Intelligence · Vol. 11 No. 1 (2022) · hlm. 265–275

DOI
10.11591/ijai.v11.i1.pp265-275
ISSN
2252-8938, 2089-4872
Terbit
2022-03-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

The appearance of agile software development techniques (ASDT) since 2001 has encouraged many organizations to move to an agile approach. ASDT presents an opportunity for researchers and professionals, but it has many challenges as well. One of the most critical challenges is agile effort prediction. Hence, many studies have investigated agile software development cost estimation (ASDCE). The objective of this study is twofold: First, to propose an improved model based on support vector regression with radial bias function kernel (SVR-RBF) enhanced by the optimized artificial immune network (Optainet). Second, to perform a detailed comparative analysis of the proposed method compared to other existing optimization techniques in the literature and applied for ASDCE. The experimental evaluation was carried out by assessing the performance of the proposed method using some trusted measures like standardized accuracy (SA), mean absolute error (MAE), prediction at level p (Pred(p)), mean balanced relative error (MBRE), mean inverted balanced relative error (MIBRE), and logarithmic standard deviation (LSD). Throughout a dataset with 21 agile projects using the leave-one-out cross-validation (LOOCV) technique. The results obtained prove that the proposed model enhances the accuracy of the SVR-RBF model, and it outperforms the majority of existing models in the literature.

Kata kunci

Jaringan kolaborasi para penulis

23 penulis · 54 tautan kepenulisan bersama · 12 lintas organisasi

Arahkan kursor atau ketuk sebuah nama untuk menyorot rekan penulisnya. Klik dua kali untuk membuka halaman penulis.

  • University of Hassan II Casablanca
  • Mohammed V University
  • Chouaib Doukkali University
  • Afiliasi tidak diketahui
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
  • Garis lebih tebal = lebih banyak artikel bersama · lingkaran lebih besar = lebih banyak artikel
Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Saïd NouhUniversity of Hassan II Casablanca5
Salsabila BenghazouaniUniversity of Hassan II Casablanca2
Jamal Eddine RafiqUniversity of Hassan II Casablanca2
Mohammed AmraouyMohammed V University2
Mustapha HainUniversity of Hassan II Casablanca2
Jihad AdibUniversity of Hassan II Casablanca1
Hicham MoutachaouikUniversity of Hassan II Casablanca1
Ihsane HaloumUniversity of Hassan II Casablanca1
Mostafa JebbarUniversity of Hassan II Casablanca1
Abdellah BennaneMohammed V University1
Mustapha OuissUniversity of Hassan II Casablanca1
Abdelaziz EttaoufikUniversity of Hassan II Casablanca1
Mohammed El AssadUniversity of Hassan II Casablanca1
Imrane Chemseddine IdrissiAfiliasi tidak diketahui1
El Mehdi BellfkihUniversity of Hassan II Casablanca1
Nawal SaelUniversity of Hassan II Casablanca1
Soukaina MerzoukChouaib Doukkali University1
Soukaina BouhsissinUniversity of Hassan II Casablanca1
Touria HamimUniversity of Hassan II Casablanca1
Ali IdriMohammed V University1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">An enhanced support vector regression model for agile projects cost estimation</dc:title>
	<dc:creator>Najm, Assia</dc:creator>
	<dc:creator>Zakrani, Abdelali</dc:creator>
	<dc:creator>Marzak, Abdelaziz</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Agile projects; Optimized artificial immune network; Software cost prediction; Software effort estimation; Support vector regression</dc:subject>
	<dc:description xml:lang="en-US">The appearance of agile software development techniques (ASDT) since 2001 has encouraged many organizations to move to an agile approach. ASDT presents an opportunity for researchers and professionals, but it has many challenges as well. One of the most critical challenges is agile effort prediction. Hence, many studies have investigated agile software development cost estimation (ASDCE). The objective of this study is twofold: First, to propose an improved model based on support vector regression with radial bias function kernel (SVR-RBF) enhanced by the optimized artificial immune network (Optainet). Second, to perform a detailed comparative analysis of the proposed method compared to other existing optimization techniques in the literature and applied for ASDCE. The experimental evaluation was carried out by assessing the performance of the proposed method using some trusted measures like standardized accuracy (SA), mean absolute error (MAE), prediction at level p (Pred(p)), mean balanced relative error (MBRE), mean inverted balanced relative error (MIBRE), and logarithmic standard deviation (LSD). Throughout a dataset with 21 agile projects using the leave-one-out cross-validation (LOOCV) technique. The results obtained prove that the proposed model enhances the accuracy of the SVR-RBF model, and it outperforms the majority of existing models in the literature.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2022-03-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/21136</dc:identifier>
	<dc:identifier>10.11591/ijai.v11.i1.pp265-275</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 11, No 1: March 2022; 265-275</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v11.i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/21136/13336</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>