An ensemble approach of simple regression models to cross-project fault prediction

Satoshi Uchigaki, Shinji Uchida, Koji Toda, Akito Monden

Research output: Chapter in Book/Report/Conference proceedingConference contribution

20 Citations (Scopus)

Abstract

In software development, prediction of fault-prone modules is an important challenge for effective software testing. However, high prediction accuracy may not be achieved in cross-project prediction, since there is a large difference in distribution of predictor variables between the base project and the target project.@In this paper we propose an prediction technique called gan ensemble of simple regression modelsh to improve the prediction accuracy of cross-project prediction. The proposed method uses weighted sum of outputs of simple logistic regression models to improve the generalization ability of logistic models. To evaluate the performance of the proposed method, we conducted cross-project prediction using datasets of projects from NASA IV&V Facility Metrics Data Program. As a result, the proposed method outperformed conventional logistic regression models in terms of AUC of the Alberg diagram.

Original languageEnglish
Title of host publicationProceedings - 13th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, SNPD 2012
Pages476-481
Number of pages6
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event13th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, SNPD 2012 - Kyoto, Japan
Duration: Aug 8 2012Aug 10 2012

Publication series

NameProceedings - 13th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, SNPD 2012

Other

Other13th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, SNPD 2012
Country/TerritoryJapan
CityKyoto
Period8/8/128/10/12

Keywords

  • empirical study
  • fault-prone module prediction
  • product metrics

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Software

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