A systematic review of machine learning methods in software testing
S Ajorloo, A Jamarani, M Kashfi, MH Kashani… - Applied Soft …, 2024 - Elsevier
Background The quest for higher software quality remains a paramount concern in software
testing, prompting a shift towards leveraging machine learning techniques for enhanced …
testing, prompting a shift towards leveraging machine learning techniques for enhanced …
Fault diagnosis of actuator damage in UAVs using embedded recorded data and stacked machine learning models
Unmanned aerial vehicles (UAVs) have gained significant importance due to their wide
applicability in modern life. Fault diagnosis plays a crucial role in ensuring their safe and …
applicability in modern life. Fault diagnosis plays a crucial role in ensuring their safe and …
[HTML][HTML] Advancing biomedical engineering: Leveraging Hjorth features for electroencephalography signal analysis
Materials and methods At the heart of this investigation lies the MILimbEEG Dataset, an
extensive collection of EEG recordings harvested from a heterogeneous group of 60 …
extensive collection of EEG recordings harvested from a heterogeneous group of 60 …
Gradient boosting optimized through differential evolution for predicting the testing effort of software projects
Software testing (ST) is one of the most important software development life cycle (SDLC)
phases and ST effort is often expressed as a percentage of SDLC effort. Unfortunately, in the …
phases and ST effort is often expressed as a percentage of SDLC effort. Unfortunately, in the …
Optimizing Complex Ensembles: Automatic Bias-Variance Tuning with SVM-Based Learners
AHA Monfared, S Sharif-Bakhtiar - 2024 20th CSI International …, 2024 - ieeexplore.ieee.org
Ensembling is an extremely powerful and popular method for increasing the strength of a
machine learning predictor by combining it with other predictors in order to obtain more …
machine learning predictor by combining it with other predictors in order to obtain more …
A systematic review of hyperparameter tuning techniques for software quality prediction models
R Malhotra, M Cherukuri - Intelligent Data Analysis - content.iospress.com
BACKGROUND: Software quality prediction models play a crucial role in identifying
vulnerable software components during early stages of development, and thereby …
vulnerable software components during early stages of development, and thereby …
Using data analytics techniques in software quality assurance
MAR Hafiz - 2023 - lutpub.lut.fi
Software quality assurance is a set of activities that ensures the quality of the software from
different aspects. This process continues in parallel with the development of the software to …
different aspects. This process continues in parallel with the development of the software to …