Earticle

초록

영어

We explore the software metrics and build a Bayesian Network Model for defect prediction. Much previous work has concentrated on how to select the software metrics that are most likely to indicate fault-proneness, based on the hypnosis that these metrics are independent. But in reality, software metric values are predicted not only correlated with fault-proneness, but also observed internal complex relationship with each other. In this paper, we build a Bayesian network model to represent the probability distribution of each factor and how they affect defects, considering strong or weak correlations are existed between individual metric attributes. We perform a comparative experimental study of effectiveness of Bayesian Network, logistic regression and Naive Bayes on a public data set from an open source software system. The result shows that our approach produces statistically significant estimations.

목차

Abstract
 1. Introduction
 2. Related work
  2.1 Analysis of software metrics
  2.2 Bayesian Network Introduction
 3. Research Method
  3.1 Model Parameters
  3.2 Construct Bayesian Network
 4. Experiments
  4.1 Data pre-processing
  4.2 Fault Proneness Analysis
 5. Conclusion and Future Work
 7. REFERENCES

키워드

저자

• Yuyang Liu [ Chonnam National University, South Korea ]
• Wooi Ping Cheah [ Chonnam National University, South Korea ]
• Byung-Ki Kim [ Chonnam National University, South Korea ]
• Hyukro Park [ Chonnam National University, South Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Advanced Science and Technology

• 간기

  월간

• pISSN

  2005-4238

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605