Software effort estimation based on open source projects: Case study of Github

2017 ◽  
Vol 92 ◽  
pp. 145-157 ◽  
Author(s):  
Fumin Qi ◽  
Xiao-Yuan Jing ◽  
Xiaoke Zhu ◽  
Xiaoyuan Xie ◽  
Baowen Xu ◽  
...  
Keyword(s):  
Open Source ◽  
Effort Estimation ◽  
Software Effort Estimation

scholarly journals Case Study on Software Effort Estimation

2017 ◽  
Vol 7 (3) ◽  
pp. 103-107 ◽  
Author(s):  
Tülin Erçelebi Ayyıldız ◽  
◽  
Hasan Can Terzi
Keyword(s):  
Effort Estimation ◽  
Software Effort Estimation

scholarly journals Developing and using checklists to improve software effort estimation: A multi-case study

2018 ◽  
Vol 146 ◽  
pp. 286-309 ◽  
Author(s):  
Muhammad Usman ◽  
Kai Petersen ◽  
Jürgen Börstler ◽  
Pedro Santos Neto
Keyword(s):  
Effort Estimation ◽  
Software Effort Estimation

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.

scholarly journals Risk analysis of gas leakage in gas pressure reduction station and its consequences: A case study for Zahedan

Heliyon ◽  
2021 ◽  
Vol 7 (5) ◽  
pp. e06911
Author(s):  
Peiman Dadkani ◽  
Esmatullah Noorzai ◽  
AmirHossein Ghanbari ◽  
Ali Gharib
Keyword(s):  
Risk Analysis ◽  
Gas Pressure ◽  
Pressure Reduction ◽  
Gas Leakage ◽  
Pressure Reduction Station
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