A software defect prediction method using binary gray wolf optimizer and machine learning algorithms
Authors:
- Hao WangHWHao Wang
- Bahman ArastehBABahman Arasteh
- Keyvan ArastehKAKeyvan Arasteh
- Farhad Soleimanian GharehchopoghFGFarhad Soleimanian Gharehchopogh
- Alireza RouhiARAlireza Rouhi
- 31 May 2024
- journal article
- research article
- Published by Elsevier in Computers and Electrical Engineering
- Vol. 118
Abstract
No abstract availableKeywords
This publication has 17 references indexed in Scilit:
- An efficient convergence-boosted salp swarm optimizer-based artificial neural network for the development of software fault prediction modelsComputers and Electrical Engineering, 2023
- SFLA‐based heuristic method to generate software structural test dataJournal of Software: Evolution and Process, 2019
- Feature selection through binary brain storm optimizationComputers and Electrical Engineering, 2018
- Programming guidelines for improving software resiliency against soft-errors without performance overheadComputing, 2018
- Software defect prediction techniques using metrics based on neural network classifierCluster Computing, 2018
- Feature selection using firefly algorithm in software defect predictionCluster Computing, 2017
- Extracting software static defect models using data miningAin Shams Engineering Journal, 2015
- Grey Wolf OptimizerAdvances in Engineering Software, 2014
- Developing Inherently Resilient Software Against Soft-Errors Based on Algorithm Level Inherent FeaturesJournal of Electronic Testing, 2014
- Software Defect Detection with RocusJournal of Computer Science and Technology, 2011