Improving the Performance of Effort Estimation in Terms of Function Point Analysis by Balancing Datasets

Abstract

This research proposes an approach to improve the performance of effort estimation based on the balancing of each group for categorical variables. The proposed model is based on function point analysis, Industry Sector, and deep learning. The Pytorch library is used to build the deep learning model with the dataset ISBSG (release 2020). The accuracy of our model is compared with that of the Adj-Effort approach. We adopt the prediction level at 0.3, Mean Absolute Error, Mean Balanced Relative Error, Mean Inverted Balanced Relative Error, and Standardised Accuracy as criteria for validation. The findings demonstrate that our proposed model outweighs the unbalanced and Adj-Effort approaches.