Estimating Software Development Efforts Using a Random Forest-Based Stacked Ensemble Approach

Electronics

卷:10

PriyaVarshini A G¹  AnithaKumari K²  Vijayakumar Varadarajan³ 

[1] Department of Information Technology, Dr. Mahalingam College of Engineering and Technology, Pollachi, Coimbatore 642 003, India;

[2] Department of Information Technology, PSG College of Technology, Coimbatore 641 004, India;

[3] School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia;

关键词: machine learning;    deep learning;    software effort estimation;    ensemble techniques;    stacked using random forest;   

DOI  :  10.3390/electronics10101195

来源: DOAJ

【 摘 要 】

Software Project Estimation is a challenging and important activity in developing software projects. Software Project Estimation includes Software Time Estimation, Software Resource Estimation, Software Cost Estimation, and Software Effort Estimation. Software Effort Estimation focuses on predicting the number of hours of work (effort in terms of person-hours or person-months) required to develop or maintain a software application. It is difficult to forecast effort during the initial stages of software development. Various machine learning and deep learning models have been developed to predict the effort estimation. In this paper, single model approaches and ensemble approaches were considered for estimation. Ensemble techniques are the combination of several single models. Ensemble techniques considered for estimation were averaging, weighted averaging, bagging, boosting, and stacking. Various stacking models considered and evaluated were stacking using a generalized linear model, stacking using decision tree, stacking using a support vector machine, and stacking using random forest. Datasets considered for estimation were Albrecht, China, Desharnais, Kemerer, Kitchenham, Maxwell, and Cocomo81. Evaluation measures used were mean absolute error, root mean squared error, and R-squared. The results proved that the proposed stacking using random forest provides the best results compared with single model approaches using the machine or deep learning algorithms and other ensemble techniques.

【 授权许可】

Unknown