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
키워드
Software QualitySoftware Project DataMissing Value ImputationEMB AlgorithmRandom Forest
저자
Takayuki Morita [ Graduate School of Science Engineering, Hosei University ]
Mitsuhiro Kimura [ Faculty of Science Engineering, Hosei University ]
Corresponding Author
보안공학연구지원센터(IJSEIA) [Science & Engineering Research Support Center, Republic of Korea(IJSEIA)]
설립연도
2006
분야
공학>컴퓨터학
소개
1. 보안공학에 대한 각종 조사 및 연구
2. 보안공학에 대한 응용기술 연구 및 발표
3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최
4. 보안공학 기술의 상호 협조 및 정보교환
5. 보안공학에 관한 표준화 사업 및 규격의 제정
6. 보안공학에 관한 산학연 협동의 증진
7. 국제적 학술 교류 및 기술 협력
8. 보안공학에 관한 논문지 발간
9. 기타 본 회 목적 달성에 필요한 사업
간행물
간행물명
International Journal of Software Engineering and Its Applications
간기
월간
pISSN
1738-9984
수록기간
2008~2016
등재여부
SCOPUS
십진분류
KDC 505DDC 605
이 권호 내 다른 논문 / International Journal of Software Engineering and Its Applications Vol.9 No.2