NDLI: A novel method for early software quality prediction based on support vector machine

Content Provider	IEEE Xplore Digital Library
Author	Fei Xing Ping Guo Lyu, M.R.
Copyright Year	2005
Description	Author affiliation: Dept. of Comput. Sci., Beijing Normal Univ. (Fei Xing; Ping Guo)
Abstract	The software development process imposes major impacts on the quality of software at every development stage; therefore, a common goal of each software development phase concerns how to improve software quality. Software quality prediction thus aims to evaluate software quality level periodically and to indicate software quality problems early. In this paper, we propose a novel technique to predict software quality by adopting support vector machine (SVM) in the classification of software modules based on complexity metrics. Because only limited information of software complexity metrics is available in early software life cycle, ordinary software quality models cannot make good predictions generally. It is well known that SVM generalizes well even in high dimensional spaces under small training sample conditions. We consequently propose a SVM-based software classification model, whose characteristic is appropriate for early software quality predictions when only a small number of sample data are available. Experimental results with a medical imaging system software metrics data show that our SVM prediction model achieves better software quality prediction than some commonly used software quality prediction models
Starting Page	10
Ending Page	222
File Size	223659
Page Count	213
File Format	PDF
ISBN	0769524826
ISSN	10719458
DOI	10.1109/ISSRE.2005.6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-11-08
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Software quality Support vector machines Predictive models Support vector machine classification Software metrics Computer science Programming Biomedical imaging Software systems Testing
Content Type	Text
Resource Type	Article

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