Multi-view learning for software defect prediction


ÖZTÜRK KIYAK E., BİRANT D., BİRANT K. U.

E-INFORMATICA SOFTWARE ENGINEERING JOURNAL, vol.15, no.1, pp.163-184, 2021 (ESCI) identifier

  • Publication Type: Article / Article
  • Volume: 15 Issue: 1
  • Publication Date: 2021
  • Doi Number: 10.37190/e-inf210108
  • Journal Name: E-INFORMATICA SOFTWARE ENGINEERING JOURNAL
  • Journal Indexes: Emerging Sources Citation Index (ESCI), Scopus, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, Directory of Open Access Journals
  • Page Numbers: pp.163-184
  • Keywords: Software defect prediction, multi-view learning, machine learning, k-nearest neighbors, MULTITASK, MODEL, KNN
  • Dokuz Eylül University Affiliated: Yes

Abstract

Background: Traditionally, machine learning algorithms have been simply applied for software defect prediction by considering single-view data, meaning the input data contains a single feature vector. Nevertheless, different software engineering data sources may include multiple and partially independent information, which makes the standard single-view approaches ineffective.