A Hybrid Model for Improving Software Cost Estimation in Global Software Development  

作　　者：Mehmood Ahmed Noraini B.Ibrahim Wasif Nisar Adeel Ahmed Muhammad Junaid Emmanuel Soriano Flores Divya Anand 

机构地区：[1]Faculty of Computer Science and Information Technology,Universiti Tun Hussein Onn Malaysia Parit Raja,Batu Pahat,86400,Malaysia [2]Department of Computer Science,COMSATS University Wah Cantt Campus,Islamabad,47010,Pakistan [3]Department of Information Technology,The University of Haripur,Khyber Pakhtunkhwa,22620,Pakistan [4]Engineering Research Innovation Group,Universidad Europea del Atlantico,Santander,39011,Spain

出　　处：《Computers, Materials & Continua》2024年第1期1399-1422,共24页计算机、材料和连续体（英文）

摘　　要：Accurate software cost estimation in Global Software Development(GSD)remains challenging due to reliance on historical data and expert judgments.Traditional models,such as the Constructive Cost Model(COCOMO II),rely heavily on historical and accurate data.In addition,expert judgment is required to set many input parameters,which can introduce subjectivity and variability in the estimation process.Consequently,there is a need to improve the current GSD models to mitigate reliance on historical data,subjectivity in expert judgment,inadequate consideration of GSD-based cost drivers and limited integration of modern technologies with cost overruns.This study introduces a novel hybrid model that synergizes the COCOMO II with Artificial Neural Networks(ANN)to address these challenges.The proposed hybrid model integrates additional GSD-based cost drivers identified through a systematic literature review and further vetted by industry experts.This article compares the effectiveness of the proposedmodelwith state-of-the-artmachine learning-basedmodels for software cost estimation.Evaluating the NASA 93 dataset by adopting twenty-six GSD-based cost drivers reveals that our hybrid model achieves superior accuracy,outperforming existing state-of-the-artmodels.The findings indicate the potential of combining COCOMO II,ANN,and additional GSD-based cost drivers to transform cost estimation in GSD.

关 键 词：Artificial neural networks COCOMO II cost drivers global software development linear regression software cost estimation 

分 类 号：TP311.5[自动化与计算机技术—计算机软件与理论]