on the predictability of software efforts using machine learning techniques

CORC > 软件研究所 > 软件所图书馆 > 会议论文

	on the predictability of software efforts using machine learning techniques
	Zhang Wen ; Yang Ye ; Wang Qing
	2011
会议名称	6th International Conference on Evaluation of Novel Approaches to Software Engineering, ENASE 2011
会议日期	June 8, 2011 - June 11, 2011
会议地点	Beijing, China
关键词	Decision trees Forecasting Neural networks Software engineering Supervised learning Unsupervised learning
页码	5-14
中文摘要	This paper investigates the predictability of software effort using machine learning techniques. We employed unsupervised learning as k-medoids clustering with different similarity measures to extract natural clusters of projects from software effort data set, and supervised learning as J48 decision tree, back propagation neural network (BPNN) and na¨ive Bayes to classify the software projects. We also investigate the impact of imputing missing values of projects on the performances of both unsupervised and supervised learning techniques. Experiments on ISBSG and CSBSG data sets demonstrate that unsupervised learning as k-medoids clustering has produced a poor performance in software effort prediction and Kulzinsky coefficient has the best performance in software effort prediction in measuring the similarities of projects. Supervised learning techniques have produced superior performances in software effort prediction. Among the three supervised learning techniques, BPNN produces the best performance. Missing data imputation has improved the performances of both unsupervised and supervised learning techniques.
英文摘要	This paper investigates the predictability of software effort using machine learning techniques. We employed unsupervised learning as k-medoids clustering with different similarity measures to extract natural clusters of projects from software effort data set, and supervised learning as J48 decision tree, back propagation neural network (BPNN) and na¨ive Bayes to classify the software projects. We also investigate the impact of imputing missing values of projects on the performances of both unsupervised and supervised learning techniques. Experiments on ISBSG and CSBSG data sets demonstrate that unsupervised learning as k-medoids clustering has produced a poor performance in software effort prediction and Kulzinsky coefficient has the best performance in software effort prediction in measuring the similarities of projects. Supervised learning techniques have produced superior performances in software effort prediction. Among the three supervised learning techniques, BPNN produces the best performance. Missing data imputation has improved the performances of both unsupervised and supervised learning techniques.
收录类别	EI
会议录	ENASE 2011 - Proceedings of the 6th International Conference on Evaluation of Novel Approaches to Software Engineering
语种	英语
ISBN号	9789898425577
内容类型	会议论文
源URL	[http://ir.iscas.ac.cn/handle/311060/16307]
专题	软件研究所_软件所图书馆_会议论文
推荐引用方式 GB/T 7714	Zhang Wen,Yang Ye,Wang Qing. on the predictability of software efforts using machine learning techniques[C]. 见:6th International Conference on Evaluation of Novel Approaches to Software Engineering, ENASE 2011. Beijing, China. June 8, 2011 - June 11, 2011.