Earticle

초록

영어

Software Project Management (SPM) is one of the primary factors to software success or failure. Prediction of software development time is the key task for the effective SPM. The accuracy and reliability of prediction mechanisms is also important. In this paper, we compare different machine learning techniques in order to accurately predict the software time. Finally, by comparing the accuracy of different techniques, it has been concluded that Gaussian process algorithm has highest prediction accuracy among other techniques studied. Experimental results show this prediction approach is more effective.

목차

Abstract
 1. Introduction
 2. Related Work
  2.1. Neural Networks
  2.2. Linear Regression
  2.3. Least Median Square
  2.4. Gaussian Process
  2.5. M5P
  2.6. REPtree
  2.7. Sequential Minimal Optimization
  2.8. Multilayer Perceptron
 3. Comparison of Machine Learning Algorithms to Predict Project Time
 4. Experimental Results
 5. Conclusions Second and Following Pages
 Acknowledgements
 References

키워드

저자

• WanJiang Han [ School of Software Engineering, Beijing University of Posts and Telecommunication, Beijing 100876, China ]
• LiXin Jiang [ Department of Emergency Response, China Earthquake Networks Center, Beijing 100036, China ]
• TianBo Lu [ School of Software Engineering, Beijing University of Posts and Telecommunication, Beijing 100876, China ]
• XiaoYan Zhang [ School of Software Engineering, Beijing University of Posts and Telecommunication, Beijing 100876, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605