基于加权类比的软件成本估算方法
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摘要
软件成本估算是软件项目开发周期、管理决策和软件项目质量中最重要的问题之一。针对软件研发成本估算在软件行业中普遍存在不准确、难以估算的问题,提出一种基于加权类比的软件成本估算方法,将相似度距离定义为具有相关性的马氏距离,通过优化的粒子群算法优化后得到权值,并用类比法估算软件成本。实验结果表明,该方法具有比非加权类比、神经网络等非计算模型方法更高的精确度。实际案例测试表明,该方法在软件开发初期基于需求分析的软件成本估算比专家估算有更精确的评估结果。
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.
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.
引文
[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 different Neural Network architectures for software cost estimation[C]∥International Conference on Computer,Control,Informatics 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(PSOFLANN)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,FERNNDEZ-ALEMN J L,et al.Software cost estimation for global software development a systematic map and review study[C]∥International Conference on Evaluation of Novel Approaches To Software Engineering.IEEE,2015:197-206.
[6]杨抒,王业,乌尔柯西,等.基于C&S-PSO的软件成本估算类比法特征权重优化[J].计算机系统应用,2015,24(7):99-103.
[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 12.5International Conference.Springer Berlin Heidelberg,2010:287-294.
[8]吴登生,李建平,蔡晨.软件成本估算的粒子群算法类比模型及自助法推断[J].管理科学,2010,23(3):113-120.
[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.
[10]王振丽.基于加权MP马氏距离的GS方法研究[D].南京:南京理工大学,2016.
[11]KENNEDY J,EBERHART R.Particle swarm optimization[C]∥IEEE International Conference on Neural Networks.IEEE,1995:1942-1948.
[12]牛利勇,张帝,王晓峰,等.基于自适应变异粒子群算法的电动出租车充电引导[J].电网技术,2015,39(1):63-68.
[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.
[14]中华人民共和国工业和信息化部.软件研发成本度量规范:SJ/T11463-2013[S].2013.
[2] SARNO R,SIDABUTAR J,SARWOSRI.Comparison of different Neural Network architectures for software cost estimation[C]∥International Conference on Computer,Control,Informatics 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(PSOFLANN)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,FERNNDEZ-ALEMN J L,et al.Software cost estimation for global software development a systematic map and review study[C]∥International Conference on Evaluation of Novel Approaches To Software Engineering.IEEE,2015:197-206.
[6]杨抒,王业,乌尔柯西,等.基于C&S-PSO的软件成本估算类比法特征权重优化[J].计算机系统应用,2015,24(7):99-103.
[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 12.5International Conference.Springer Berlin Heidelberg,2010:287-294.
[8]吴登生,李建平,蔡晨.软件成本估算的粒子群算法类比模型及自助法推断[J].管理科学,2010,23(3):113-120.
[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.
[10]王振丽.基于加权MP马氏距离的GS方法研究[D].南京:南京理工大学,2016.
[11]KENNEDY J,EBERHART R.Particle swarm optimization[C]∥IEEE International Conference on Neural Networks.IEEE,1995:1942-1948.
[12]牛利勇,张帝,王晓峰,等.基于自适应变异粒子群算法的电动出租车充电引导[J].电网技术,2015,39(1):63-68.
[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.
[14]中华人民共和国工业和信息化部.软件研发成本度量规范:SJ/T11463-2013[S].2013.