A Comparative Analysis of Regression Models for Software Effort Estimation

https://doi.org/10.55529/ijitc.36.26.42

Authors

  • Md. Tanziar Rahman Department of Software Engineering, Nuclear Power Plant Company Bangladesh Limited, Bangladesh
  • Md. Motaharul Islam Department of Computer Science & Engineering, United International University, Bangladesh
  • Ummay Salma Shorna Department of Statistics, Central Bank of Bangladesh

Keywords:

Software Effort Estimation, Ridge, Lasso, Elastic Net, Random Forest, Support Vector Regression.

Abstract

Software Effort Estimation is the utmost task in software engineering and project management. This is important to estimate cost properly and the number of people required for a project to be developed. Many techniques have been used to estimate cost, time, schedule and required manpower for software development industries. Nowadays software is developed in a more complex way and its success depends on efficient estimation techniques. In this research, we have compared five regression algorithms on different projects to estimate software effort. The main advantage of these models is they can be used in the early stages of the software life cycle and that can be helpful to project managers to conduct effort estimation efficiently before starting the project. It avoids project overestimation and late delivery. Software size, productivity, complexity and requirement stability are the input vectors for these regression models. The estimated efforts have been calculated using Ridge Regression, Lasso Regression, Elastic Net, Random Forest and Support Vector Regression. We have compared unitedly these models for the first time as software effort estimators. R-squared Score, Mean Squared Error (MSE) and Mean Absolute Error (MAE) are calculated for these regression models. Ridge, Lasso and Elastic Net show comparatively better results among others.

Published

2023-10-01

How to Cite

Md. Tanziar Rahman, Md. Motaharul Islam, & Ummay Salma Shorna. (2023). A Comparative Analysis of Regression Models for Software Effort Estimation. International Journal of Information Technology & Computer Engineering , 3(06), 26–42. https://doi.org/10.55529/ijitc.36.26.42

Issue

Section

Aricle Publication

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