Computer Science ›› 2018, Vol. 45 ›› Issue (11A): 501-504.

• Software Engineering & Database Technology • Previous Articles     Next Articles

Software Cost Estimation Method Based on Weighted Analogy

ZHAO Xiao-min, CAO Guang-bin, FEI Meng-yu, ZHU Li-nan   

  1. College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China
  • Online:2019-02-26 Published:2019-02-26

Abstract: Software cost estimation is one of the most important issues in the cycle of development,management decision,and in the quality of software project.Aiming at the common problems of software cost estimation in the software industry,such as inaccuracy of cost estimation and estimation difficulty,this paper presented a weighted analogy-based software cost estimation method.In this method,the similarity distance is defined as the Mahalanobis distance with correlation,and the weight is obtained by particle swarm optimization.The software cost is estimated by analogy method.The result shows that this method has high accuracy compared with non-computational based model methods such as non-weighted analogy and neural networks.At the same time,the actual cases show that this method is more accurate than expert estimation in software cost estimation based on demand analysis at the early stage of software development.

Key words: Software cost estimation, Mahalanobis distance, Weighted analogy, Particle swarm optimization

CLC Number: 

  • TP391
[1]DAVE V S,DUTTA K.Neural network based models for software effort estimation:a review[J].Artificial Intelligence Review,2014,42(2):295-307.
[2]SARNO R,SIDABUTAR J,SARWOSRI.Comparison of diffe-rent Neural Network architectures for software cost estimation[C]∥International Conference on Computer,Control,Informa-tics and ITS Applications.IEEE,2016:68-73.
[3]WANI Z H,QUADRI S M K.Artificial Bee Colony-Trained Functional Link Artificial Neural Network Model for Software Cost Estimation[M]∥Proceedings of Fifth International Conference on Soft Computing for Problem Solving.2016:729-741.
[4]BENALA T R,CHINNABABU K,MALL R,et al.A particle swarm optimized functional link artificial neural network (PSO-FLANN) in software cost estimation[C]∥Proceedings of the International Conference on Frontiers of Intelligent Computing:Theory and Applications (FICTA) Advances in Intelligent Systems and Computing.Springer Berlin Heidelberg,2013:59-66.
[5]BAJTA M E,IDRI A,FERNÁNDEZ-ALEMÁN J L,et al.Software cost estimation for global software development a syste-matic map and review study[C]∥International Conference on Evaluation of Novel Approaches To Software Engineering.IEEE,2015:197-206.
[7]PAPATHEOCHAROUS E,ANDREOU A S.On the Problem of Attribute Selection for Software Cost Estimation:Input Backward Elimination Using Artificial Neural Networks[C]∥Artificial Intelligence Applications and Innovations,Ifip Wg 12.5 International Conference.Springer Berlin Heidelberg,2010:287-294.
[9]DIZAJI Z A,KHALILPOUR K.Particle Swarm Optimization and Chaos Theory Based Approach for Software Cost Estimation[J].International Journal of Academic Research,2014,6(3):130.
[11]KENNEDY J,EBERHART R.Particle swarm optimization[C]∥IEEE International Conference on Neural Networks.IEEE,1995:1942-1948.
[13]DESHARNAIS J M.Analyse statistique de la productivitie des projets informatique a partie de la technique des point des fonction[D].Quebec:University of Montreal,1989.
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