Neural network and statistical modeling of software development effort

Ruchi Shukla; Mukul Shukla; Tshilidzi Marwala

doi:10.1007/978-81-322-1602-5_21

Neural network and statistical modeling of software development effort

Ruchi Shukla, Mukul Shukla, Tshilidzi Marwala

School of Mechanical and Aerospace Engineering

Research output: Contribution to journal › Article

1 Citation (Scopus)

Abstract

Many modeling studies that aimed at providing an accurate relationship between the software project effort (or cost) and the involved cost drivers have been conducted for effectivemanagement of software projects. However, the derived models are only applicable for a specific project and its variables. In this chapter, we present the use of back-propagation neural network (NN) to model the software development (SD) effort of 18 SD NASA projects based on six cost drivers. The performance of the NN model was also compared with a multi-regression model and other models available in the literature. © Springer India 2014.

Original language	English
Pages (from-to)	189-198
Journal	Advances in Intelligent Systems and Computing
Volume	236
Early online date	26 Feb 2014
DOIs	https://doi.org/10.1007/978-81-322-1602-5_21
Publication status	Published - 2014

Bibliographical note

cited By 0; Conference of 2nd International Conference on Soft Computing for Problem Solving, SocProS 2012 ; Conference Date: 28 December 2012 Through 30 December 2012; Conference Code:116609

Keywords

Backpropagation
Costs
NASA
Neural networks
Problem solving
Regression analysis
Soft computing
Software engineering, Back propagation neural networks
Effort Estimation
Multi-regression model
NASA projects
Regression
Software development effort
Software project
Statistical modeling, Software design

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Cite this

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title = "Neural network and statistical modeling of software development effort",

abstract = "Many modeling studies that aimed at providing an accurate relationship between the software project effort (or cost) and the involved cost drivers have been conducted for effectivemanagement of software projects. However, the derived models are only applicable for a specific project and its variables. In this chapter, we present the use of back-propagation neural network (NN) to model the software development (SD) effort of 18 SD NASA projects based on six cost drivers. The performance of the NN model was also compared with a multi-regression model and other models available in the literature. {\textcopyright} Springer India 2014.",

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author = "Ruchi Shukla and Mukul Shukla and Tshilidzi Marwala",

note = "cited By 0; Conference of 2nd International Conference on Soft Computing for Problem Solving, SocProS 2012 ; Conference Date: 28 December 2012 Through 30 December 2012; Conference Code:116609",

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doi = "10.1007/978-81-322-1602-5_21",

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AU - Shukla, Mukul

AU - Marwala, Tshilidzi

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N2 - Many modeling studies that aimed at providing an accurate relationship between the software project effort (or cost) and the involved cost drivers have been conducted for effectivemanagement of software projects. However, the derived models are only applicable for a specific project and its variables. In this chapter, we present the use of back-propagation neural network (NN) to model the software development (SD) effort of 18 SD NASA projects based on six cost drivers. The performance of the NN model was also compared with a multi-regression model and other models available in the literature. © Springer India 2014.

AB - Many modeling studies that aimed at providing an accurate relationship between the software project effort (or cost) and the involved cost drivers have been conducted for effectivemanagement of software projects. However, the derived models are only applicable for a specific project and its variables. In this chapter, we present the use of back-propagation neural network (NN) to model the software development (SD) effort of 18 SD NASA projects based on six cost drivers. The performance of the NN model was also compared with a multi-regression model and other models available in the literature. © Springer India 2014.

KW - Backpropagation

KW - Costs

KW - NASA

KW - Neural networks

KW - Problem solving

KW - Regression analysis

KW - Soft computing

KW - Software engineering, Back propagation neural networks

KW - Effort Estimation

KW - Multi-regression model

KW - NASA projects

KW - Regression

KW - Software development effort

KW - Software project

KW - Statistical modeling, Software design

U2 - 10.1007/978-81-322-1602-5_21

DO - 10.1007/978-81-322-1602-5_21

M3 - Article

SN - 2194-5357

VL - 236

SP - 189

EP - 198

JO - Advances in Intelligent Systems and Computing

JF - Advances in Intelligent Systems and Computing

ER -

Neural network and statistical modeling of software development effort

Abstract

Bibliographical note

Keywords

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