Software Effort Estimation of Web Applications Using Fuzzy Cognitive Mapping

2021 ◽  
Author(s):  
Sharma Ashutosh ◽  
Alexey Tselykh
Keyword(s):  
Web Applications ◽  
Cognitive Mapping ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Fuzzy Cognitive Mapping

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.

Unveiling the dynamics of social credit determinants using fuzzy cognitive mapping and system dynamics

2021 ◽  
Author(s):  
Leandro F. Pereira ◽  
Ricardo J. C. Correia ◽  
Fernando A. F. Ferreira ◽  
Pedro F. Falcão ◽  
Renato L. Costa
Keyword(s):  
System Dynamics ◽  
Cognitive Mapping ◽  
Fuzzy Cognitive Mapping ◽  
Social Credit

Examining the Feasibility of a Case-Based Reasoning Model for Software Effort Estimation

MIS Quarterly ◽  
1992 ◽  
Vol 16 (2) ◽  
pp. 155 ◽  
Author(s):  
Tridas Mukhopadhyay ◽  
Steven S. Vicinanza ◽  
Michael J. Prietula
Keyword(s):  
Case Based Reasoning ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Case Based

Software Effort Estimation Using ISBSG Dataset: Multiple Case Studies

2021 ◽  
Author(s):  
Huseyin Unlu ◽  
Ali Gorkem Yalcin ◽  
Dilek Ozturk ◽  
Guliz Akkaya ◽  
Mert Kalecik ◽  
...  
Keyword(s):  
Case Studies ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Multiple Case ◽  
Multiple Case Studies
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