DEFECT SEVERITY CODE PREDICTION BASED ON ENSEMBLE LEARNING
In machine learning, learning algorithms that learn from other algorithms are called meta-learning. New algorithms called Ensemble algorithms have surfaced as a viable method to improve defect prediction models' accuracy and dependability. In software development defect prediction of software...
Saved in:
| Main Authors: | Ghada Mohammad Tahir Aldabbagh, Safwan Omar Hasoon |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Lublin University of Technology
2024-12-01
|
| Series: | Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska |
| Subjects: | |
| Online Access: | https://ph.pollub.pl/index.php/iapgos/article/view/6393 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Leveraging an Enhanced CodeBERT-Based Model for Multiclass Software Defect Prediction via Defect Classification
by: Rida Ghafoor Hussain, et al.
Published: (2025-01-01) -
Adaptive Ensemble Learning Model-Based Binary White Shark Optimizer for Software Defect Classification
by: Jameel Saraireh, et al.
Published: (2025-01-01) -
Cross-Project Defect Prediction: A Literature Review
by: Sourabh Pal, et al.
Published: (2022-01-01) -
Current Trends in Class Imbalance Learning for Software Defect Prediction
by: Somya R. Goyal
Published: (2025-01-01) -
Software Defect Prediction Using an Intelligent Ensemble-Based Model
by: Misbah Ali, et al.
Published: (2024-01-01)