An empirical validation of a neural network model for software effort estimation

Park, Heejun; Baek, Seung Hwa

doi:10.1016/j.eswa.2007.08.001

Cited by 117 publications

(62 citation statements)

References 23 publications

(37 reference statements)

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“…However, when using ANN one crucial step is to identify the dominant cost factors, or attributes, that affect development effort [7]. A number of measures exist to determine the significance of ANN input attributes [1,8,9, 10] but we identified that they have never been applied for software cost drivers.…”

Section: Related Workmentioning

confidence: 99%

On the Problem of Attribute Selection for Software Cost Estimation: Input Backward Elimination Using Artificial Neural Networks

Papatheocharous

Andreou

2010

IFIP Advances in Information and Communication Technology

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Abstract. Many parameters affect the cost evolution of software projects. In the area of software cost estimation and project management the main challenge is to understand and quantify the effect of these parameters, or 'cost drivers', on the effort expended to develop software systems. This paper aims at investigating the effect of cost attributes on software development effort using empirical databases of completed projects and building Artificial Neural Network (ANN) models to predict effort. Prediction performance of various ANN models with different combinations of inputs is assessed in an attempt to reduce the models' input dimensions. The latter is performed by using one of the most popular saliency measures of network weights, namely Garson's Algorithm. The proposed methodology provides an insight on the interpretation of ANN which may be used for capturing nonlinear interactions between variables in complex software engineering environments.

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Section: Related Workmentioning

confidence: 99%

On the Problem of Attribute Selection for Software Cost Estimation: Input Backward Elimination Using Artificial Neural Networks

Papatheocharous

Andreou

2010

IFIP Advances in Information and Communication Technology

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“…Additionally, none of the previous work used neural network models to predict software effort from use case diagrams. On the other hand, References (Pendharkar et al, 2005;Papatheocharous and Andreou, 2007;Kumar et al, 2008;de Barcelos Tronto et al, 2008;Park and Baek, 2008;Attarzadeh and Ow, 2011;Idri et al, 2008Idri et al, , 2010Reddy et al, 2008;Shin and Goel, 2000) used neural network models such as MLP and RBFNN to predict software estimation. References (Azzeh et al, 2010(Azzeh et al, , 2011Huang and Chiu, 2006) used soft computing techniques with analogy based estimation, whereas References (Idri and Abran, 2000;Huang et al, 2007) used soft computing with algorithmic models.…”

Section: Related Workmentioning

confidence: 99%

Towards an early software estimation using log-linear regression and a multilayer perceptron model

Nassif

Ho²,

Capretz

2013

Journal of Systems and Software

166

160

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This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues.Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. a b s t r a c tSoftware estimation is a tedious and daunting task in project management and software development. Software estimators are notorious in predicting software effort and they have been struggling in the past decades to provide new models to enhance software estimation. The most critical and crucial part of software estimation is when estimation is required in the early stages of the software life cycle where the problem to be solved has not yet been completely revealed. This paper presents a novel log-linear regression model based on the use case point model (UCP) to calculate the software effort based on use case diagrams. A fuzzy logic approach is used to calibrate the productivity factor in the regression model. Moreover, a multilayer perceptron (MLP) neural network model was developed to predict software effort based on the software size and team productivity. Experiments show that the proposed approach outperforms the original UCP model. Furthermore, a comparison between the MLP and log-linear regression models was conducted based on the size of the projects. Results demonstrate that the MLP model can surpass the regression model when small projects are used, but the log-linear regression model gives better results when estimating larger projects.

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“…Other example of neural networks usefulness was provided by Park and Baek Park & Baek (2008), who investigated 148 software projects completed between 1999 and 2003 by one of the Korean IT service vendors. The authors compared accuracy of the neural networks model with human experts' judgments and two classical regression models.…”

Section: Neural Networkmentioning

confidence: 99%

Automatic Control of the Software Development Process with Regard to Risk Analysis

Jureczko¹

2012

Automation

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An empirical validation of a neural network model for software effort estimation

Cited by 117 publications

References 23 publications

On the Problem of Attribute Selection for Software Cost Estimation: Input Backward Elimination Using Artificial Neural Networks

On the Problem of Attribute Selection for Software Cost Estimation: Input Backward Elimination Using Artificial Neural Networks

Towards an early software estimation using log-linear regression and a multilayer perceptron model

Automatic Control of the Software Development Process with Regard to Risk Analysis

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