Optimizing Design of Software Size Estimation model using Neural Network

Optimizing Design of Software Size Estimation model using Neural Network

© 2022 by IJETT Journal
Volume-70 Issue-12
Year of Publication : 2022
Author : Manisha, Rahul Rishi, Sonia Sharma, Renu
DOI : 10.14445/22315381/IJETT-V70I12P215

How to Cite?

Manisha, Rahul Rishi, Sonia Sharma, Renu, "Optimizing Design of Software Size Estimation model using Neural Network," International Journal of Engineering Trends and Technology, vol. 70, no. 12, pp. 138-146, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I12P215

Size Estimation has always been an area of interest in the software industry. Estimating size directly could lead to the calculation of storage identities and costs. This paper proposes a neural network-based size estimation method which utilizes the architecture of Machine Learning. In this paper, the k-means algorithm is used to divide the data into multiple segments, which is further utilized by the Fuzzy logic-based inference engine to generate the class labels. In this model, the NASA-based PROMISE Dataset has been utilized, and there is no class label containing the project size. In order to validate the class label, the collected data is passed to a multi-class classifier which uses the Levenberg principle. The proposed model is evaluated using quantitative parameters, namely the class and overall class accuracy, and is compared with other classification architectures. The accuracy of the proposed model has been improved by 9.7% in comparison with other techniques and 0.7% in comparison to existing studies

Class accuracy, Fuzzy logic, Machine learning, Neural network, Size estimation.

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