Comparing Multiple Linear Regression, Deep Learning and Multiple Perceptron for Functional Points Estimation

doi:10.1109/ACCESS.2022.3215987

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Comparing Multiple Linear Regression, Deep Learning and Multiple Perceptron for Functional Points Estimation

2022-01-01

DOI：10.1109/ACCESS.2022.3215987

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Abstract

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This study compares the performance of Pytorch-based Deep Learning, Multiple Perceptron Neural Networks with Multiple Linear Regression in terms of software effort estimations based on function point analysis. This study investigates Adjusted Function Points, Function Point Categories, Industry Sector, and Relative Size. The ISBSG dataset (version 2020/R1) is used as the historical dataset. The effort estimation performance is compared among multiple models by evaluating a prediction level of 0.30 and standardized accuracy. According to the findings, the Multiple Perceptron Neural Network based on Adjusted Function Points combined with Industry Sector predictors yielded 53% and 61% in terms of standardized accuracy and a prediction level of 0.30, respectively. The findings of Pytorch-based Deep Learning are similar to Multiple Perceptron Neural Networks, with even better results than that, with standardized accuracy and a prediction level of 0.30, 72% and 72%, respectively. The results reveal that both the Pytorch-based Deep Learning and Multiple Perceptron model outperformed Multiple Linear Regression and baseline models using the experimental dataset. Furthermore, in the studied dataset, Adjusted Function Points may not contribute to higher accuracy than Function Point Categories.

Keywords:

Software engineering

Estimation

Mathematical models

Machine learning

Linear regression

Neural networks

Complexity theory

Software effort estimation

function point analysis

industry sector

relative size