Artificial Neural Network Architecture and Orthogonal Arrays in Estimation of Software Projects Efforts

Nevena Ranković; Dragica Rankovic; Mirjana Ivanovic; Ljubomir Lazic

doi:10.1109/INISTA52262.2021.9548402

Artificial Neural Network Architecture and Orthogonal Arrays in Estimation of Software Projects Efforts

Nevena Ranković, Dragica Rankovic, Mirjana Ivanovic, Ljubomir Lazic

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

Abstract

Accurate assessment of software project development using the proper artificial intelligence tools can be a significant challenge for success in the software industry. This paper aims to minimize the relative error in software estimation using the proposed model of an artificial neural network (ANN) based on Taguchi's orthogonal vector plan. By selecting methods of clustering and fuzzification of different project values within several used datasets such as COCOMO2000, NASA60, and Kemerer15, reducing the number and time of iterations minimizes Mean Magnitude Relative Error (MMRE) and include a wide range of observed data. Additional criteria, such as monitoring prediction, correlation, and comparison with RBF (Radial Basis Function) relative error, were used to confirm that the proposed model gives two to three times better results depending on the observed cluster. Based on the obtained results, the accuracy and reliability of the proposed model for estimating software projects were determined

Original language	English
Title of host publication	International Conference on INnovations in Intelligent SysTems and Applications, Technically Co-Sponsored by the IEEE SMC Society, Kocaeli, Turkey
Publisher	IEEE
Pages	1-6
Number of pages	6
ISBN (Electronic)	978-1-6654-3603-8
ISBN (Print)	978-1-6654-1162-2
DOIs	https://doi.org/10.1109/INISTA52262.2021.9548402
Publication status	Published - 2021
Externally published	Yes
Event	2021 International Conference on INnovations in Intelligent SysTems and Applications, Technically Co-Sponsored by the IEEE SMC Society, August 25-27, Kocaeli, Turkey - Duration: 25 Aug 2021 → …

Conference

Conference	2021 International Conference on INnovations in Intelligent SysTems and Applications, Technically Co-Sponsored by the IEEE SMC Society, August 25-27, Kocaeli, Turkey
Period	25/08/21 → …

Keywords

Accurate Assessment
Artificial Intelligence
Artificial Neural Network Design
Taguchi's Orthogonal Vector

Access to Document

10.1109/INISTA52262.2021.9548402

Cite this

Ranković, N., Rankovic, D., Ivanovic, M., & Lazic, L. (2021). Artificial Neural Network Architecture and Orthogonal Arrays in Estimation of Software Projects Efforts. In International Conference on INnovations in Intelligent SysTems and Applications, Technically Co-Sponsored by the IEEE SMC Society, Kocaeli, Turkey (pp. 1-6). IEEE. https://doi.org/10.1109/INISTA52262.2021.9548402

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title = "Artificial Neural Network Architecture and Orthogonal Arrays in Estimation of Software Projects Efforts",

abstract = "Accurate assessment of software project development using the proper artificial intelligence tools can be a significant challenge for success in the software industry. This paper aims to minimize the relative error in software estimation using the proposed model of an artificial neural network (ANN) based on Taguchi's orthogonal vector plan. By selecting methods of clustering and fuzzification of different project values within several used datasets such as COCOMO2000, NASA60, and Kemerer15, reducing the number and time of iterations minimizes Mean Magnitude Relative Error (MMRE) and include a wide range of observed data. Additional criteria, such as monitoring prediction, correlation, and comparison with RBF (Radial Basis Function) relative error, were used to confirm that the proposed model gives two to three times better results depending on the observed cluster. Based on the obtained results, the accuracy and reliability of the proposed model for estimating software projects were determined",

keywords = "Accurate Assessment, Artificial Intelligence, Artificial Neural Network Design, Taguchi's Orthogonal Vector",

author = "Nevena Rankovi{\'c} and Dragica Rankovic and Mirjana Ivanovic and Ljubomir Lazic",

year = "2021",

doi = "10.1109/INISTA52262.2021.9548402",

language = "English",

isbn = "978-1-6654-1162-2",

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booktitle = "International Conference on INnovations in Intelligent SysTems and Applications, Technically Co-Sponsored by the IEEE SMC Society, Kocaeli, Turkey",

publisher = "IEEE",

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Ranković, N, Rankovic, D, Ivanovic, M & Lazic, L 2021, Artificial Neural Network Architecture and Orthogonal Arrays in Estimation of Software Projects Efforts. in International Conference on INnovations in Intelligent SysTems and Applications, Technically Co-Sponsored by the IEEE SMC Society, Kocaeli, Turkey. IEEE, pp. 1-6, 2021 International Conference on INnovations in Intelligent SysTems and Applications, Technically Co-Sponsored by the IEEE SMC Society, August 25-27, Kocaeli, Turkey, 25/08/21. https://doi.org/10.1109/INISTA52262.2021.9548402

Artificial Neural Network Architecture and Orthogonal Arrays in Estimation of Software Projects Efforts. / Ranković, Nevena; Rankovic, Dragica; Ivanovic, Mirjana et al.
International Conference on INnovations in Intelligent SysTems and Applications, Technically Co-Sponsored by the IEEE SMC Society, Kocaeli, Turkey. IEEE, 2021. p. 1-6.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Artificial Neural Network Architecture and Orthogonal Arrays in Estimation of Software Projects Efforts

AU - Ranković, Nevena

AU - Rankovic, Dragica

AU - Ivanovic, Mirjana

AU - Lazic, Ljubomir

PY - 2021

Y1 - 2021

N2 - Accurate assessment of software project development using the proper artificial intelligence tools can be a significant challenge for success in the software industry. This paper aims to minimize the relative error in software estimation using the proposed model of an artificial neural network (ANN) based on Taguchi's orthogonal vector plan. By selecting methods of clustering and fuzzification of different project values within several used datasets such as COCOMO2000, NASA60, and Kemerer15, reducing the number and time of iterations minimizes Mean Magnitude Relative Error (MMRE) and include a wide range of observed data. Additional criteria, such as monitoring prediction, correlation, and comparison with RBF (Radial Basis Function) relative error, were used to confirm that the proposed model gives two to three times better results depending on the observed cluster. Based on the obtained results, the accuracy and reliability of the proposed model for estimating software projects were determined

AB - Accurate assessment of software project development using the proper artificial intelligence tools can be a significant challenge for success in the software industry. This paper aims to minimize the relative error in software estimation using the proposed model of an artificial neural network (ANN) based on Taguchi's orthogonal vector plan. By selecting methods of clustering and fuzzification of different project values within several used datasets such as COCOMO2000, NASA60, and Kemerer15, reducing the number and time of iterations minimizes Mean Magnitude Relative Error (MMRE) and include a wide range of observed data. Additional criteria, such as monitoring prediction, correlation, and comparison with RBF (Radial Basis Function) relative error, were used to confirm that the proposed model gives two to three times better results depending on the observed cluster. Based on the obtained results, the accuracy and reliability of the proposed model for estimating software projects were determined

KW - Accurate Assessment

KW - Artificial Intelligence

KW - Artificial Neural Network Design

KW - Taguchi's Orthogonal Vector

U2 - 10.1109/INISTA52262.2021.9548402

DO - 10.1109/INISTA52262.2021.9548402

M3 - Conference contribution

SN - 978-1-6654-1162-2

SP - 1

EP - 6

BT - International Conference on INnovations in Intelligent SysTems and Applications, Technically Co-Sponsored by the IEEE SMC Society, Kocaeli, Turkey

PB - IEEE

T2 - 2021 International Conference on INnovations in Intelligent SysTems and Applications, Technically Co-Sponsored by the IEEE SMC Society, August 25-27, Kocaeli, Turkey

Y2 - 25 August 2021

ER -

Artificial Neural Network Architecture and Orthogonal Arrays in Estimation of Software Projects Efforts

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Keywords

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