[HTML][HTML] Predicting Software Defects in Hybrid MPI and OpenMP Parallel Programs Using Machine Learning

AS Althiban, HM Alharbi, LA Al Khuzayem, FE Eassa - Electronics, 2023 - mdpi.com
High-performance computing (HPC) and its supercomputers are essential for solving the
most difficult issues in many scientific computing domains. The proliferation of computational …

Comprehensive Survey of different Machine Learning Algorithms used for Software Defect Prediction

V Gururaj, KR Umadi, M Kumar… - … on Decision Aid …, 2022 - ieeexplore.ieee.org
The software development life cycle is a long and complicated process. It consists of
analysis, design, development, testing and deployment. Defect prediction is the technique of …

A Classification Framework to Detect Sars Covid-19 Disease Using Feature Selection and Variant-Based Ensemble Learning

A Akhtar - International Journal of Computational and Innovative …, 2023 - ijcis.com
The hazardous COVID-19 pandemic has caused millions of deaths worldwide which depicts
the significance of an early screening of this infection in order to stop it from spreading. Real …

Optimization of association rules using hybrid data mining technique

SP Shankar, E Naresh, H Agrawal - Innovations in Systems and Software …, 2022 - Springer
Software quality has been the important area of interest for decades now in the IT sector and
software firms. Defect prediction gives the tester the pointers as to where the bugs will most …

[PDF][PDF] SMS Spam Detection Using Multiple Linear Regression and Extreme Learning Machines

ZH Ali, HM Salman, AH Harif - Iraqi Journal of Science, 2023 - iasj.net
With the growth of the use mobile phones, people have become increasingly interested in
using Short Message Services (SMS) as the most suitable communications service. The …

A novel multiple ensemble learning models based on different datasets for software defect prediction

A Nawaz, AU Rehman, M Abbas - arXiv preprint arXiv:2008.13114, 2020 - arxiv.org
Software testing is one of the important ways to ensure the quality of software. It is found that
testing cost more than 50% of overall project cost. Effective and efficient software testing …

Improved mayfly optimization deep stacked sparse auto encoder feature selection scorched gradient descent driven dropout XLM learning framework for software …

M Anbu - Concurrency and Computation: Practice and …, 2022 - Wiley Online Library
Software testing is the process of improving software quality by classifying and removing
defects in the software development. Previously, several methods were used for software …

A Survey on Feature Selection in Imbalanced Data for Software Defect Prediction

M Abdullah, FA Setiawan - 2023 Eighth International …, 2023 - ieeexplore.ieee.org
In software defect prediction, unbalanced data poses a substantial obstacle due to the
considerably lesser count of corrupted instances in comparison to non-damaged instances …

[PDF][PDF] Homogenous multiple classifier system for software quality assessment based on support vector machine

UG Inyang, OS Adeoye, EN Udo, EF Bassey… - Computer and …, 2022 - academia.edu
In today's society, almost all human endeavours depend on software products. Lack of
quality software is one of the software industry's most important problems. Hence, it would …

Multi-layer perceptron neural network with feature selection for software defect prediction

JM Catherine, S Djodilatchoumy - 2021 2nd International …, 2021 - ieeexplore.ieee.org
Software is continuously evolving and hence it is essential for the production of quality and
stable software by every software provider. Recently there is a paradigm shift in how …