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Title:
A study on software effort prediction using machine learning techniques
Author: Zhang, Wen (1) ; Yang, Ye (1) ; Wang, Qing (1)
Conference Name: 6th International Conference Evaluation of Novel Approaches to Software Engineering, ENASE 2011
Conference Date: June 8, 2011 - June 11, 2011
Issued Date: 2013
Conference Place: Beijing, China
Publish Place: Springer Verlag, Tiergartenstrasse 17, Heidelberg, D-69121, Germany
Indexed Type: EI
ISSN: 18650929
ISBN: 9783642323409
Department: (1) Laboratory for Internet Software Technologies, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China
Abstract: This paper conducts a study on of software effort prediction using machine learning techniques. Both supervised and unsupervised learning techniques are employed to predict software effort using historical dataset. The unsupervised learning as k-medoids clustering equipped with different similarity measures is used to cluster projects in historical dataset. The supervised learning as J48 decision tree, back propagation neural network (BPNN) and na¨ive Bayes is used to classify the software projects into different effort classes. We also impute the missing values in the historical datasets and then machine learning techniques are adopted to predict software effort. Experiments on ISBSG and CSBSG datasets demonstrate that unsupervised learning as k-medoids clustering produced a poor performance. Kulzinsky coefficient has the best performance in measuring the similarities of projects. Supervised learning techniques produced superior performances than unsupervised learning techniques in software effort prediction. BPNN produced the best performance among the three supervised learning techniques. Missing data imputation improved the performances of both unsupervised and supervised learning techniques in software effort prediction. © Springer-Verlag Berlin Heidelberg 2013.
English Abstract: This paper conducts a study on of software effort prediction using machine learning techniques. Both supervised and unsupervised learning techniques are employed to predict software effort using historical dataset. The unsupervised learning as k-medoids clustering equipped with different similarity measures is used to cluster projects in historical dataset. The supervised learning as J48 decision tree, back propagation neural network (BPNN) and na¨ive Bayes is used to classify the software projects into different effort classes. We also impute the missing values in the historical datasets and then machine learning techniques are adopted to predict software effort. Experiments on ISBSG and CSBSG datasets demonstrate that unsupervised learning as k-medoids clustering produced a poor performance. Kulzinsky coefficient has the best performance in measuring the similarities of projects. Supervised learning techniques produced superior performances than unsupervised learning techniques in software effort prediction. BPNN produced the best performance among the three supervised learning techniques. Missing data imputation improved the performances of both unsupervised and supervised learning techniques in software effort prediction. © Springer-Verlag Berlin Heidelberg 2013.
Language: 英语
Content Type: 会议论文
URI: http://ir.iscas.ac.cn/handle/311060/16675
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 . A study on software effort prediction using machine learning techniques[C]. 见:6th International Conference Evaluation of Novel Approaches to Software Engineering, ENASE 2011. Beijing, China. June 8, 2011 - June 11, 2011.
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