Neuro fuzzy—COCOMO II model for software cost estimation

Kaur, Ishleen; Narula, Gagandeep Singh; Wason, Ritika; Jain, Vishal; Baliyan, Anupam

doi:10.1007/s41870-018-0083-6

Cited by 46 publications

(8 citation statements)

References 12 publications

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“…For the first phase of our experiment, which involves training the input signals, a COCOMO2000 dataset 37 of 94 projects was used. In the second phase of data testing, the same dataset was used, but taking into consideration other 20 projects.…”

Section: Applied Methodology—taguchi Robust Design and Ann Architecturementioning

confidence: 99%

Influence of input values on the prediction model error using artificial neural network based on Taguchi's orthogonal array

Ranković

Ivanović

et al. 2022

Concurrency and Computation

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Rapid and accurate assessment of software project development using artificial intelligence tools can be essential for success in the software industry. This article has two objectives: to reduce the magnitude relative error (MRE) value in estimating the effort and cost of software development using the proposed artificial neural network architecture based on the Taguchi method and examine the influence of input variables on the change in relative error value. Clustering and fuzzification methods further mitigate the heterogeneous structure of the different project values of the datasets used.Taguchi method contributes to the reduction of the number of iterations by 99%, which achieves a significant reduction in estimation and value of MRE. By monitoring additional criteria, such as prediction, correlation, and comparing two activation functions, such as sigmoid and radial basis function, the proposed model's correctness, reliability, and stability are confirmed. Significantly better results are expected using the sigmoid activation function and a decrease in the value of the mean (MRE).

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Section: Applied Methodology—taguchi Robust Design and Ann Architecturementioning

confidence: 99%

Influence of input values on the prediction model error using artificial neural network based on Taguchi's orthogonal array

Ranković

Ivanović

et al. 2022

Concurrency and Computation

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“…The changes impact the project's achievement and fulfillment. In agile software development (ASD) the project manager welcomes the changes at any stage of the project [1,13,14]. The changes impact a task from numerous points of view because the changes have risks related to it as far as cost, time, and completion of the project.…”

Section: Existing Techniquesmentioning

confidence: 99%

Prediction based cost estimation technique in agile development

Butt

Ercan²,

Binsawad³

et al. 2023

Advances in Engineering Software

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“…Research by [ 20 , 30 ] represents the use of a combined technique, as they combine an application of ML that improves the COCOMO model using ANNs; however, the results achieved for MRE were again relatively high. An interesting study was conducted on different neural network algorithms, and their comparison to accurately estimate software costs.…”

Section: Related Workmentioning

confidence: 99%

Improved Effort and Cost Estimation Model Using Artificial Neural Networks and Taguchi Method with Different Activation Functions

Ranković

Ivanović

et al. 2021

Entropy

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Software estimation involves meeting a huge number of different requirements, such as resource allocation, cost estimation, effort estimation, time estimation, and the changing demands of software product customers. Numerous estimation models try to solve these problems. In our experiment, a clustering method of input values to mitigate the heterogeneous nature of selected projects was used. Additionally, homogeneity of the data was achieved with the fuzzification method, and we proposed two different activation functions inside a hidden layer, during the construction of artificial neural networks (ANNs). In this research, we present an experiment that uses two different architectures of ANNs, based on Taguchi’s orthogonal vector plans, to satisfy the set conditions, with additional methods and criteria for validation of the proposed model, in this approach. The aim of this paper is the comparative analysis of the obtained results of mean magnitude relative error (MMRE) values. At the same time, our goal is also to find a relatively simple architecture that minimizes the error value while covering a wide range of different software projects. For this purpose, six different datasets are divided into four chosen clusters. The obtained results show that the estimation of diverse projects by dividing them into clusters can contribute to an efficient, reliable, and accurate software product assessment. The contribution of this paper is in the discovered solution that enables the execution of a small number of iterations, which reduces the execution time and achieves the minimum error.

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Neuro fuzzy—COCOMO II model for software cost estimation

Cited by 46 publications

References 12 publications

Influence of input values on the prediction model error using artificial neural network based on Taguchi's orthogonal array

Influence of input values on the prediction model error using artificial neural network based on Taguchi's orthogonal array

Prediction based cost estimation technique in agile development

Improved Effort and Cost Estimation Model Using Artificial Neural Networks and Taguchi Method with Different Activation Functions

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