Introduction to Effort Estimation

2014 ◽  
pp. 27-53 ◽  
Author(s):  
Emilia Mendes
Keyword(s):  
Effort Estimation

Programming Effort Estimation.

1984 ◽  
Author(s):  
S. D. Conte ◽  
H. E. Dunsmore ◽  
V. Y. Shen
Keyword(s):  
Effort Estimation ◽  
Programming Effort

Feasibility and effort estimation models for medium and small size information mining projects

2015 ◽  
Vol 47 ◽  
pp. 1-14 ◽  
Author(s):  
Pablo Pytel ◽  
Alejandro Hossian ◽  
Paola Britos ◽  
Ramón García-Martínez
Keyword(s):  
Effort Estimation ◽  
Information Mining ◽  
Size Information ◽  
Mining Projects ◽  
Estimation Models

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

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1195
Author(s):  
Priya Varshini A G ◽  
Anitha Kumari K ◽  
Vijayakumar Varadarajan
Keyword(s):  
Deep Learning ◽  
Random Forest ◽  
Software Development ◽  
Weighted Averaging ◽  
Software Project ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Single Model ◽  
Ensemble Techniques ◽  
Project Estimation

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.

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