Performance Evaluation of Various ANN Architectures Using Proposed Cost Function

Sandhya,; Ghose, Udayan; Bisht, Upasana

doi:10.1109/icrito48877.2020.9197851

Cited by 2 publications

(1 citation statement)

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“…A and B are the baseline constants for calibration; KSLOC (thousands of Source Lines of Code) represents the size of the software project; SF j stands for five scale factors; EM i stands for seventeen effort multipliers; Cost drivers of the COCOMO2000 Post Architecture model together comprise these scale factors and effort multipliers. In [19], [20] the authors used the Back-Propagation Neural Networks technique through COCOMO dataset & NASA 2 dataset which consist of 60 and 93 projects respectively and the sigmoid function to evaluate software cost. The result showed less MRE value.…”

Section: Literature Reviewmentioning

confidence: 99%

A New Approach to Software Effort Estimation Using Different Artificial Neural Network Architectures and Taguchi Orthogonal Arrays

Ranković

Ivanović

et al. 2021

IEEE Access

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In this paper, two different architectures of Artificial Neural Networks (ANN) are proposed as an efficient tool for predicting and estimating software effort. Artificial Neural Networks, as a branch of machine learning, are used in estimation because they tend towards fast learning and giving better and more accurate results. The search/optimization embraced here is motivated by the Taguchi method based on Orthogonal Arrays (an extraordinary set of Latin Squares), which demonstrated to be an effective apparatus in a robust design. This paper aims to minimize the magnitude relative error (MRE) in effort estimation by using Taguchi's Orthogonal Arrays, as well as to find the simplest possible architecture of an artificial Neural Network for optimized learning. A descending gradient (GA) criterion has also been introduced to know when to stop performing iterations. Given the importance of estimating software projects, our work aims to cover as many different values of actual efficiency of a wide range of projects as possible by division into clusters and a certain coding method, in addition to the mentioned tools. In this way, the risk of error estimation can be reduced, to increase the rate of completed software projects.

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Section: Literature Reviewmentioning

confidence: 99%