Software Fault Prediction Models for Web Applications

作者: Le Truong Giang , Dongwon Kang , Doo-Hwan Bae

DOI: 10.1109/COMPSACW.2010.19

关键词:

摘要: Our daily life increasingly relies on Web applications. applications provide us with abundant services to support our everyday activities. As a result, quality assurance for is becoming important and has gained much attention from software engineering community. In recent years, in order enhance quality, many fault prediction models have been constructed predict which modules are likely be faulty during operations. Such can utilized raise the effectiveness of testing activities reduce project risks. Although current applied applications, one limitation them that they do not consider particular characteristics this paper, we try build aiming after analyzing major may impact their quality. The experimental study shows approach achieves very promising results.

参考文章(25)
Mark Andrew Hall, Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning international conference on machine learning. pp. 359- 366 ,(2000)
Dursun Delen, David L. Olson, Advanced Data Mining Techniques ,(2008)
Mark A. Hall, Ian H. Witten, Eibe Frank, Data Mining: Practical Machine Learning Tools and Techniques ,(1999)
Daniel T Larose, Daniel T Larose, Data Mining Methods and Models ,(2006)
Peter Dolog, Maristella Matera, Florian Daniel, Sven Casteleyn, Engineering Web Applications ,(2009)
Karim O. Elish, Mahmoud O. Elish, Predicting defect-prone software modules using support vector machines Journal of Systems and Software. ,vol. 81, pp. 649- 660 ,(2008) , 10.1016/J.JSS.2007.07.040
F. Ricca, P. Tonella, Detecting anomaly and failure in Web applications IEEE MultiMedia. ,vol. 13, pp. 44- 51 ,(2006) , 10.1109/MMUL.2006.26
Erik Arisholm, Lionel C. Briand, Eivind B. Johannessen, A systematic and comprehensive investigation of methods to build and evaluate fault prediction models Journal of Systems and Software. ,vol. 83, pp. 2- 17 ,(2010) , 10.1016/J.JSS.2009.06.055