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 h...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.展开更多
Metaheuristic approaches in cloud computing have shown significant results due to theirmulti-objective advantages.These approaches are now considering hybridmetaheuristics combining the relative optimized benefits of ...Metaheuristic approaches in cloud computing have shown significant results due to theirmulti-objective advantages.These approaches are now considering hybridmetaheuristics combining the relative optimized benefits of two or more algorithms resulting in the least tradeoffs among several factors.The critical factors such as execution time,throughput time,response time,energy consumption,SLA violations,communication overhead,makespan,and migration time need careful attention while designing such dynamic algorithms.To improve such factors,an optimizedmulti-objective hybrid algorithm is being proposed that combines the relative advantages of Cat Swarm Optimization(CSO)with machine learning classifiers such as Support Vector Machine(SVM).The adopted approach is based on SVMone to many classification models of machine learning that performs the classifications of various data format types in the cloud with best accuracy.In CSO,grouping phase is used to divide the data files as audio,video,image,and text which is further extended by polynomial Kernel function based on various input features and used for optimized load balancing.Overall,proposed approach works well and achieved performance efficiency in evaluated QoS metrics such as average energy consumption by 12%,migration time by 9%,and optimization time by 10%,in the presence of competitor baselines.展开更多
文摘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.
基金This work was funded by the University of Jeddah,Saudi Arabia.The authors,therefore,acknowledge with thanks to the University technical support.The authors extend their appreciation to the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through the Project Number MoE-IF-20-01.
文摘Metaheuristic approaches in cloud computing have shown significant results due to theirmulti-objective advantages.These approaches are now considering hybridmetaheuristics combining the relative optimized benefits of two or more algorithms resulting in the least tradeoffs among several factors.The critical factors such as execution time,throughput time,response time,energy consumption,SLA violations,communication overhead,makespan,and migration time need careful attention while designing such dynamic algorithms.To improve such factors,an optimizedmulti-objective hybrid algorithm is being proposed that combines the relative advantages of Cat Swarm Optimization(CSO)with machine learning classifiers such as Support Vector Machine(SVM).The adopted approach is based on SVMone to many classification models of machine learning that performs the classifications of various data format types in the cloud with best accuracy.In CSO,grouping phase is used to divide the data files as audio,video,image,and text which is further extended by polynomial Kernel function based on various input features and used for optimized load balancing.Overall,proposed approach works well and achieved performance efficiency in evaluated QoS metrics such as average energy consumption by 12%,migration time by 9%,and optimization time by 10%,in the presence of competitor baselines.
