scholarly journals The consistency of empirical comparisons of regression and analogy-based software project cost prediction

2005 ◽  
Author(s):  
C. Mair ◽  
M. Shepperd
Keyword(s):  
Software Project ◽  
Cost Prediction ◽  
Project Cost

A study of software development project cost, schedule and quality by outsourcing to low cost destination

2016 ◽  
Vol 29 (3) ◽  
pp. 454-478 ◽  
Author(s):  
Debasisha Mishra ◽  
Biswajit Mahanty
Keyword(s):  
Software Development ◽  
Development Project ◽  
Dynamics Simulation ◽  
Software Project ◽  
Design Work ◽  
Project Cost ◽  
Content Type ◽  
Software Development Project ◽  
Study Results ◽  
Testing Work

Purpose – The purpose of this paper is to find good values of onsite-offshore team strength; number of hours of communication between business users and onsite team and between onsite and offshore team so as to reduce project cost and improve schedule in a global software development (GSD) environment for software development project. Design/methodology/approach – This study employs system dynamics simulation approach to study software project characteristics in both co-located and distributed development environments. The authors consulted 14 experts from Indian software outsourcing industry during our model construction and validation. Findings – The study results show that there is a drop in overall team productivity in outsourcing environment by considering the offshore options. But the project cost can be reduced by employing the offshore team for coding and testing work only with minimal training for imparting business knowledge. The research results show that there is a potential to save project cost by being flexible in project schedule. Research limitations/implications – The implication of the study is that the project management team should be careful not to keep high percentage of manpower at offshore location in distributed software environment. A large offshore team can increase project cost and schedule due to higher training overhead, lower productivity and higher error proneness. In GSD, the management effort should be to keep requirement analysis and design work at onsite location and involves the offshore team in coding and testing work. Practical implications – The software project manager can use the model results to divide the software team between onsite and offshore location during various phases of software development in distributed environment. Originality/value – The study is novel as there is little attempt at finding the team distribution between onsite and offshore location in GSD environment.

scholarly journals Simulator for Optimization of Software Project Cost and Schedule

2008 ◽  
Vol 4 (12) ◽  
pp. 1030-1035
Author(s):  
P.K. Suri ◽  
Bharat Bhushan
Keyword(s):  
Software Project ◽  
Project Cost

Software Project Cost Estimation Based on Groupware

2009 ◽  
Author(s):  
Yongqin Jin ◽  
Jun Li ◽  
Jianming Lin ◽  
Qingzhang Chen
Keyword(s):  
Cost Estimation ◽  
Software Project ◽  
Project Cost

scholarly journals A Whole Process Cost Prediction System for Construction Projects Based on Improved Support Vector Machines

2022 ◽  
Vol 16 ◽  
pp. 278--286
Author(s):  
Xueqing Zhang ◽  
Jie Song ◽  
Chaolin Zha
Keyword(s):  
Support Vector Machine ◽  
Construction Projects ◽  
Construction Project ◽  
Principal Component ◽  
Support Vector ◽  
Prediction System ◽  
Cost Prediction ◽  
Project Cost ◽  
Whole Process ◽  
Process Cost

The current project cost system requires high data scale, small amount of data and large prediction deviation. In order to improve the prediction accuracy of the whole process cost of construction project, this paper designs a whole process project cost prediction system based on improved support vector machine. In the hardware part of the system, the control core adopts arm controller S3C6410 and introduces 4G communication module to analyze the actual engineering data with the support of hardware. In the software part, the whole process cost prediction index system of the construction project is established, the index is reduced by the principal component method, and the support vector machine is improved by particle swarm optimization algorithm to realize the whole process cost prediction of the project. The system function test results show that the average prediction deviation of the designed system is 4.11%, the average prediction deviation of the cost prediction system is 3.05%, and the average prediction deviation of the system is 1.57%.

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