Abstract
The process of improvement software quality began from early stages of software engineering development. It uses multiple quality metrics which are very important in software development. To calculate the standards quality of in software testing has been adopted. The software testing is focusing on the Software defect. In this paper is proposed new methods which combine the particle swarm optimization (PSO) to handle the best features Extraction with back-propagation networks to testing and evaluation of the data set . The paper depended database for NASA standards data. The result and experiment method improved quality performance for all classification methods used in the research"Combining Particle Swarm Optimization based Feature Selection and Bagging for Software Defect Prediction ".
