Software quality is a field of study and practice that describes the desirable attributes of software products. The performance must be perfect without any defects.Software quality metrics are a subset of software metrics that focus on the quality aspects of the product, process, and project.The software defectprediction model helps in early detection of defects and contributes to their efficient removal and producing a quality software system based on several metrics. The main objective of paper is to help developers identify defects based on existing software metrics using data mining techniques and thereby improve the software quality.In this paper, variousclassification techniquesare revisitedwhich are employed for software defect prediction using software metrics in the literature.
목차
Abstract 1. Introduction 2. Software Metric 3. Software Defect Prediction 4. Software Defect Prediction (SDP) using Different Classification Techniques 4.1 SDP using Supervised Learning 4.2 SDP using Semi-supervised Learning 4.3. SDP using Unsupervised Learning 4.4. SDP using Machine Learning Algorithm 5. Conclusion and Future Scope References
보안공학연구지원센터(IJDTA) [Science & Engineering Research Support Center, Republic of Korea(IJDTA)]
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 Database Theory and Application
간기
격월간
pISSN
2005-4270
수록기간
2008~2016
십진분류
KDC 505DDC 605
이 권호 내 다른 논문 / International Journal of Database Theory and Application Vol.8 No.3