The effectiveness of supervised machine learning algorithms in predicting software refactoring
Refactoring is the process of changing the internal structure of software to improve its quality
without modifying its external behavior. Empirical studies have repeatedly shown that …
without modifying its external behavior. Empirical studies have repeatedly shown that …
Predictive models in software engineering: Challenges and opportunities
Predictive models are one of the most important techniques that are widely applied in many
areas of software engineering. There have been a large number of primary studies that …
areas of software engineering. There have been a large number of primary studies that …
An extended machine learning technique for polycystic ovary syndrome detection using ovary ultrasound image
Polycystic ovary syndrome (PCOS) is the most prevalent endocrinological abnormality and
one of the primary causes of anovulatory infertility in women globally. The detection of …
one of the primary causes of anovulatory infertility in women globally. The detection of …
Software defect prediction based on enhanced metaheuristic feature selection optimization and a hybrid deep neural network
K Zhu, S Ying, N Zhang, D Zhu - Journal of Systems and Software, 2021 - Elsevier
Software defect prediction aims to identify the potential defects of new software modules in
advance by constructing an effective prediction model. However, the model performance is …
advance by constructing an effective prediction model. However, the model performance is …
Performance analysis of feature selection methods in software defect prediction: a search method approach
Software Defect Prediction (SDP) models are built using software metrics derived from
software systems. The quality of SDP models depends largely on the quality of software …
software systems. The quality of SDP models depends largely on the quality of software …
[HTML][HTML] Early quality classification and prediction of battery cycle life in production using machine learning
An accurate determination of the product quality is one of the key challenges in lithium-ion
battery (LIB) production. Since LIBs are complex, electrochemical systems, conventional …
battery (LIB) production. Since LIBs are complex, electrochemical systems, conventional …
An empirical study on pareto based multi-objective feature selection for software defect prediction
The performance of software defect prediction (SDP) models depend on the quality of
considered software features. Redundant features and irrelevant features may reduce the …
considered software features. Redundant features and irrelevant features may reduce the …
Using embedded feature selection and CNN for classification on CCD-INID-V1—A new IoT dataset
As Internet of Things (IoT) networks expand globally with an annual increase of active
devices, providing better safeguards to threats is becoming more prominent. An intrusion …
devices, providing better safeguards to threats is becoming more prominent. An intrusion …
The impact of feature selection techniques on effort‐aware defect prediction: An empirical study
Abstract Effort‐Aware Defect Prediction (EADP) methods sort software modules based on
the defect density and guide the testing team to inspect the modules with high defect density …
the defect density and guide the testing team to inspect the modules with high defect density …
A comprehensive comparative study of clustering-based unsupervised defect prediction models
Software defect prediction recommends the most defect-prone software modules for
optimization of the test resource allocation. The limitation of the extensively-studied …
optimization of the test resource allocation. The limitation of the extensively-studied …