Multi-view learning for software defect prediction
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 indep...
Enregistré dans:
Auteurs principaux: | Elife Ozturk Kiyak, Derya Birant, Kokten Ulas Birant |
---|---|
Format: | article |
Langue: | EN |
Publié: |
Wroclaw University of Science and Technology
2021
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/24ab5bfe8ea24ec68f62a57a46c2184d |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
Deep Learning Software Defect Prediction Methods for Cloud Environments Research
par: Wenjian Liu, et autres
Publié: (2021) -
Surface Defect Detection of Seals Based on K-Means Clustering Algorithm and Particle Swarm Optimization
par: Xiaoguang Li, et autres
Publié: (2021) -
Analog Circuit Soft Fault Diagnosis Based on Sparse Random Projections and K-Nearest Neighbor
par: Jian Sun, et autres
Publié: (2021) -
The Model of Makerspace Development Element and Performance Analysis Based on NVivo Classification
par: Yingyan Wang, et autres
Publié: (2021) -
CT Imaging in the Diagnosis of Lung Injury of Organophosphorus Poisoning and Analysis of Its Correlation with Procalcitonin and C-Reactive Protein Levels
par: Wenwen Sun, et autres
Publié: (2021)