Earticle

초록

영어

Software fault prediction models using supervised learning cannot be applied when training data are not present. In this case, new models using unsupervised learning such as clustering algorithms are quite necessary. Nevertheless, there exist very few studies about unsupervised models because it is difficult to construct the models. One of the difficulties is to decide the number of clusters. To solve this problem, we build unsupervised models using clustering algorithms, EM and Xmeans, which determine the number of clusters automatically and compare them with results of earlier studies. Experimental results show the Xmeans model outperforms the other models.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Model Construction
  3.1. Clustering Algorithms
  3.2. Process of Model Construction
 4. Experimental Study
  4.1. Experimental Setting
  4.2. Performance Measure
  4.3. Experimental Result
 5. Conclusions
 Acknowledgements
 References

키워드

저자

• Mikyeong Park [ School of Information Technology, Sungshin Women’s University, Korea ]
• Euyseok Hong [ School of Information Technology, Sungshin Women’s University, Korea ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Software Engineering and Its Applications

• 간기

  월간

• pISSN

  1738-9984

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605