Cross-Project Fault Prediction using Artificial Intelligence

  • Niharika N Govinda Department of Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysore, India
  • Lohith R Department of Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysore, India
  • Ratnam Kumar Jha Department of Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysore, India
  • H L Gururaj Department of Information Technology, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India
DOI: https://doi.org/10.61797/ijbic.v2i1.204
Keywords: Machine Learning (ML), Artificial Neural Networks (ANN), Decision tree, Deep Learning (DL), Random forest algorithm

Abstract

Software defect prediction project focuses on finding errors or flaws in software and aiming to improve accuracy which gives evolution batch with detectable results while adding to modern outcomes and advancement liability foretelling defective code regions can assist initiators with recognizing bugs and arrange their test activities. The percentage of groups providing the legitimate foretelling is fundamental for early identification.

Published
2023-02-06
Issue
Vol 2 No 1 (2023): Int J Bioinfor Intell Comput
Section
Articles

Copyright (c) 2023 Niharika N Govinda, Lohith R, Ratnam Kumar Jha, H L Gururaj

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