The influence of machine learning on the predictive performance of cross-project defect prediction: empirical analysis
This empirical investigation delves into the influence of machine learning (ML) algorithms in the realm of cross-project defect prediction, employing the AEEEEM dataset as a foundation. The primary objective is to discern the nuanced influences of various algorithms on predictive performance, with a...
Main Authors: | Bala, Yahaya Zakariyau, Samat, Pathiah Abdul, Sharif, Khaironi Yatim, Manshor, Noridayu |
---|---|
Format: | Article |
Language: | English |
Published: |
Universitas Ahmad Dahlan
2024
|
Online Access: | http://psasir.upm.edu.my/id/eprint/113069/1/113069.pdf |
Similar Items
-
Cross-project software defect prediction
by: Bala, Yahaya Zakariyau, et al.
Published: (2022) -
Cross-project software defect prediction through multiple
learning
by: Zakariyau Bala, Yahaya, et al.
Published: (2024) -
Improving cross-project software defect prediction method through transformation and feature selection approach
by: Bala, Yahaya Zakariyau, et al.
Published: (2023) -
Wafer defect prediction with statistical machine learning
by: Arnold, Naomi (Naomi Aiko)
Published: (2016) -
Machine learning: tasks, modern day applications and challenges
by: Aljuaid, Lamyaa Zaed, et al.
Published: (2019)