Software development cost estimation using wavelet neural networks

Kumar, K. Vinay; Ravi, Vadlamani; Carr, Mahil; Kiran, N. Raj

doi:10.1016/j.jss.2007.12.793

Cited by 119 publications

(23 citation statements)

References 29 publications

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“…Notice that these are the best results obtained for the NASA data set regarding the PRED(25) (in comparison with the results obtained by Elish [30], Shin and Goel [12], Oliveira [10], Braga et al [13] and [25]). Furthermore, notice that for this data set all but one method of Table 5 is accurate according to the criterion stated by Kumar et al [27]. All methods obtained MMRE 60.…”

Section: Nasa Data Setmentioning

confidence: 52%

“…According to Kumar et al [27] a software cost model is considered acceptably accurate if MMRE is at most 0.25 and PRED (25) is at least 75%.…”

Section: Fitness Functionmentioning

confidence: 99%

“…Several methods have been investigated for software effort estimation, including traditional methods such as the constructive cost model (COCOMO) [11], and, more recently, machine learning techniques such as radial basis function (RBF) neural networks [12], MLP neural networks [26], multiple additive regression trees [30], wavelet neural networks [27], bagging predictors [13] and support vector regression (SVR) [10]. Machine learning techniques use data from past projects to build a 0950-5849/$ -see front matter Ó 2010 Elsevier B.V. All rights reserved.…”

Section: Introductionmentioning

confidence: 99%

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GA-based method for feature selection and parameters optimization for machine learning regression applied to software effort estimation

Oliveira

Braga

Lima

et al. 2010

Information and Software Technology

194

117

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Section: Nasa Data Setmentioning

confidence: 52%

“…According to Kumar et al [27] a software cost model is considered acceptably accurate if MMRE is at most 0.25 and PRED (25) is at least 75%.…”

Section: Fitness Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

GA-based method for feature selection and parameters optimization for machine learning regression applied to software effort estimation

Oliveira

Braga

Lima

et al. 2010

Information and Software Technology

194

117

View full text Add to dashboard Cite

“…To estimate software development cost, Heiat (2002) utilizes a Gaussian function, rather than a logistic sigmoid, within the hidden layer. Kumar, Ravi, Carr, and Kiran (2008) and Dejaeger et al (2012) test both the logistic sigmoid and Gaussian functions, finding that the logistic sigmoid is more accurate in predicting software development costs.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

An Investigation of Nonparametric Data Mining Techniques for Acquisition Cost Estimating

Brown¹,

White²

2017

Defense ARJ

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The Department of Defense (DoD) cost estimating methodology traditionally focuses on parametric estimating using ordinary least squares (OLS) regression. Given the recent advances in acquisition data collection, however, senior leaders have expressed an interest in incorporating "data mining" and "more innovative analyses" within cost estimating. Thus, the goal of this research is to investigate nonparametric data mining techniques and their application to DoD cost estimating. Using a meta-analysis of 14 cost estimating studies containing 32 datasets that predominantly relate to commercial software development, the predictive accuracy of OLS regression is measured against three nonparametric data mining techniques. The meta-analysis results indicate that, on average, the nonparametric techniques outperform OLS regression for cost estimating. Follow-on data mining research that incorporates DoD-specific acquisition cost data is recommended to extend this article's findings.

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“…Further, Lefley and Shepperd [9] applied genetic programming to improve software cost estimation on public datasets with great success. Later, Vinay kumar et al [15] used wavelet neural networks for the prediction of software cost estimation. Unfortunately the accuracy of these models is not satisfactory so there is always a scope for more accurate software cost estimation techniques.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison and Analysis of Different Software Cost Estimation Methods

Kumari¹,

Pushkar²

2013

IJACSA

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Abstract-Software cost estimation is the process of predicting the effort required to develop a software system. The basic input for the software cost estimation is coding size and set of cost drivers, the output is Effort in terms of Person-Months (PM's). Here, the use of support vector regression (SVR) has been proposed for the estimation of software project effort. We have used the COCOMO dataset and our results are compared to Intermediate COCOMO as well as to MOPSO model results for this dataset. It has been observed from the simulation that SVR outperforms other estimating techniques. This paper provides a comparative study on support vector regression (SVR), Intermediate COCOMO and Multiple Objective Particle Swarm Optimization (MOPSO) model for estimation of software project effort.We have analyzed in terms of accuracy and Error rate. Here, data mining tool Weka is used for simulation.

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Software development cost estimation using wavelet neural networks

Cited by 119 publications

References 29 publications

GA-based method for feature selection and parameters optimization for machine learning regression applied to software effort estimation

GA-based method for feature selection and parameters optimization for machine learning regression applied to software effort estimation

An Investigation of Nonparametric Data Mining Techniques for Acquisition Cost Estimating

Comparison and Analysis of Different Software Cost Estimation Methods

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