원문정보
보안공학연구지원센터(IJSEIA)
International Journal of Software Engineering and Its Applications
Vol.9 No.2
2015.02
pp.211-218
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
As our goal, we are interested in estimating the degree of software reliability based on software development project data. It is widely-known that several software development attributes which are measured can be used to evaluate and predict software reliability/quality via multi-variable analyses. In this article, we focus on the data treatment method which is needed prior to the software reliability assessment, since the software development data sets often include missing data. This paper discusses the method of data preparation against missing data and their effectiveness by using the Random Forest as a multi-variable analysis.
목차
Abstract
Introduction
Software Project Data with Missing Values
Preparation of the data sets
Consideration of missing patterns
Multi-variable Analysis by Random Forest
Estimation Results by Random Forest
Concluding Remarks
Acknowledgments
References
Introduction
Software Project Data with Missing Values
Preparation of the data sets
Consideration of missing patterns
Multi-variable Analysis by Random Forest
Estimation Results by Random Forest
Concluding Remarks
Acknowledgments
References
저자정보
참고문헌
자료제공 : 네이버학술정보