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Comparing Multiple Linear Regression, Deep Learning and Multiple Perceptron for Functional Points Estimation
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DOI：10.1109/ACCESS.2022.3215987.png)
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摘要
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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.
Keyword:
Software engineering
Estimation
Mathematical models
Machine learning
Linear regression
Neural networks
Complexity theory
Software effort estimation
function point analysis
industry sector
relative size
multiple perceptron neural network
multiple linear regression
software work effort
one-hot encoding
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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