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Title:
on the predictability of software efforts using machine learning techniques
Author: Zhang Wen ; Yang Ye ; Wang Qing
Source: ENASE 2011 - Proceedings of the 6th International Conference on Evaluation of Novel Approaches to Software Engineering
Conference Name: 6th International Conference on Evaluation of Novel Approaches to Software Engineering, ENASE 2011
Conference Date: June 8, 2011 - June 11, 2011
Issued Date: 2011
Conference Place: Beijing, China
Keyword: Decision trees ; Forecasting ; Neural networks ; Software engineering ; Supervised learning ; Unsupervised learning
Indexed Type: EI
ISBN: 9789898425577
Department: (1) Laboratory for Internet Software Technologies Institute of Software Chinese Academy of Sciences Beijing 100190 China
Abstract: 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.
English Abstract: 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.
Language: 英语
Content Type: 会议论文
URI: http://ir.iscas.ac.cn/handle/311060/16307
Appears in Collections:软件所图书馆_会议论文

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Recommended Citation:
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.
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