Multi-view learning for software defect prediction

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

E-INFORMATICA SOFTWARE ENGINEERING JOURNAL, cilt.15, sa.1, ss.163-184, 2021 (ESCI) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 15 Sayı: 1
  • Basım Tarihi: 2021
  • Doi Numarası: 10.37190/e-inf210108
  • Dergi Adı: E-INFORMATICA SOFTWARE ENGINEERING JOURNAL
  • Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, Directory of Open Access Journals
  • Sayfa Sayıları: ss.163-184
  • Anahtar Kelimeler: Software defect prediction, multi-view learning, machine learning, k-nearest neighbors, MULTITASK, MODEL, KNN
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

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.