The effectiveness of supervised machine learning algorithms in predicting software refactoring

M Aniche, E Maziero, R Durelli… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
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 …

Predictive models in software engineering: Challenges and opportunities

Y Yang, X Xia, D Lo, T Bi, J Grundy… - ACM Transactions on …, 2022 - dl.acm.org
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 …

An extended machine learning technique for polycystic ovary syndrome detection using ovary ultrasound image

SA Suha, MN Islam - Scientific Reports, 2022 - nature.com
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 …

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 …

Performance analysis of feature selection methods in software defect prediction: a search method approach

AO Balogun, S Basri, SJ Abdulkadir, AS Hashim - applied sciences, 2019 - mdpi.com
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 …

[HTML][HTML] Early quality classification and prediction of battery cycle life in production using machine learning

S Stock, S Pohlmann, FJ Günter, L Hille… - Journal of Energy …, 2022 - Elsevier
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 …

An empirical study on pareto based multi-objective feature selection for software defect prediction

C Ni, X Chen, F Wu, Y Shen, Q Gu - Journal of Systems and Software, 2019 - Elsevier
The performance of software defect prediction (SDP) models depend on the quality of
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

Z Liu, N Thapa, A Shaver, K Roy, M Siddula, X Yuan… - Sensors, 2021 - mdpi.com
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 …

The impact of feature selection techniques on effort‐aware defect prediction: An empirical study

F Li, W Lu, JW Keung, X Yu, L Gong, J Li - IET Software, 2023 - Wiley Online Library
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 …

A comprehensive comparative study of clustering-based unsupervised defect prediction models

Z Xu, L Li, M Yan, J Liu, X Luo, J Grundy… - Journal of Systems and …, 2021 - Elsevier
Software defect prediction recommends the most defect-prone software modules for
optimization of the test resource allocation. The limitation of the extensively-studied …