Proposing a new software cost estimation model based on artificial neural networks

Attarzadeh, Iman; Ow, Siew Hock

doi:10.1109/iccet.2010.5485840

Cited by 52 publications

(20 citation statements)

References 9 publications

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“…Artificial neural network (ANN) [3] is a form of electronic network that comprises of nodes called as "neurons" (Fig. 2).…”

Section: Neural Networkmentioning

confidence: 99%

Empirical analysis of software quality prediction using a TRAINBFG algorithm

Pattnaik¹,

Pattanayak²

2018

IJET

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Software quality plays a major role in software fault proneness. That's why prediction of software quality is essential for measuring the anticipated faults present in the software. In this paper we have proposed a Neuro-Fuzzy model for prediction of probable values for a predefined set of software characteristics by virtue of using a rule base. In course of it, we have used several training algorithms among which TRAINBFG algorithm is observed to be the best one for the purpose. There are various training algorithm available in MATLAB for training the neural network input data set. The prediction using fuzzy logic and neural network provides better result in comparison with only neural network. We find out from our implementation that TRAINBFG algorithm can provide better predicted value as compared to other algorithm in MATLAB. We have validated this result using the tools like SPSS and MATLAB.

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“…Artificial neural network (ANN) [3] is a form of electronic network that comprises of nodes called as "neurons" (Fig. 2).…”

Section: Neural Networkmentioning

confidence: 99%

Empirical analysis of software quality prediction using a TRAINBFG algorithm

Pattnaik¹,

Pattanayak²

2018

IJET

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“…The intelligent modeling methods in software metrics could be grouped into four categories, i.e., statistical methods [13], machine learning [16], neural networks [17], [18] and decision-making methods based on multicriteria [19]. The common purpose of these models is to extract the mapping relationship between the input data and the output data.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid eBusiness Software Metrics Framework for Decision Making in Cloud Computing Environment

Zhao

Nian

Jin

et al. 2017

IEEE Systems Journal

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Developing high-quality software is essential for eBusiness organizations to cope with drastic market competition. With the development of cloud computing technologies, eBusiness systems and applications pay more attention to open endedness. In a cloud computing environment, eBusiness systems have the ability to provide information technology resources on demand. Traditional software metric methods in distributed systems and applications are technical and project driven, making the market demand and internal practical operation not perfectly balanced within a cloud-computing-based eBusiness corporation. To address this issue, this paper presents a hybrid framework based on the goal/question/metric paradigm to evaluate the quality and efficiency of previous software products, projects, and development organizations in a cloud computing environment. In our approach, to support decision making at the project and organization levels, three angular metrics are used, i.e., project metrics, product metrics, and organization metrics. Furthermore, an improved radial-basis-function-based model is also provided to manage existing projects and design new projects. Experimental results on a well-known eBusiness organization show that the proposed framework is effective, efficient, and operational. Moreover, using the described decision-making algorithm, the predicted data are very close to actual results on the software cost, the fault rate, the development workload, etc., which are greatly helpful in achieving high-quality software.

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“…In the simulation study of this paper that is based on Finnish data set and the author opted the log-linear model. In [4], Iman Attarzedh et al proposed a New Software Cost Estimation Model based on Artificial Neural network. In [5] the effort estimation is evaluated on the basis of genetic algorithm.…”

Section: Background and Related Workmentioning

confidence: 99%

Prediction of Software Project Effort Estimation: A Case Study

Sadiq¹,

Asim²,

Ahmed³

et al. 2011

IJMO

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It is well known fact that predicting software effort for the software development projects with any acceptable degree remains challenging. In this paper we have used the organic software projects because in each case the projects size lies between 2-50 KLOC. In this paper we have applied the linear regression model i.e. Effort = -1.5 + 0.1804 FP to predict the software project effort and function point, and on the basis of the fuzzy logic we have also predicted the software project effort. After obtaining the software effort, project manager can arrange the project progress, control the cost and ensures the quality more accurately.

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Proposing a new software cost estimation model based on artificial neural networks

Cited by 52 publications

References 9 publications

Empirical analysis of software quality prediction using a TRAINBFG algorithm

Empirical analysis of software quality prediction using a TRAINBFG algorithm

A Hybrid eBusiness Software Metrics Framework for Decision Making in Cloud Computing Environment

Prediction of Software Project Effort Estimation: A Case Study

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