In software defect prediction, noisy attributes and high-dimensional data remain to be a
critical challenge. This paper introduces a novel approach known as multi correlation-based …

In software defect prediction, noisy attributes and high-dimensional data remain to be a
critical challenge. This paper introduces a novel approach known as multi correlation-based …

Software defect prediction models are used for predicting high risk software components.
Feature selection has significant impact on the prediction performance of the software defect …

To address the challenges associated with the abundance of features in software datasets,
this study proposes a novel hybrid feature selection method that combines quantum particle …

In this digital world, using software has become an important part of daily life and business.
The software must be rigorously tested in order to avert a financial crisis. The defect-free …

High dimensionality and class imbalance are latent data quality problems that have a
negative effect on the predictive capabilities of prediction models in software defect …

The high dimensionality of software metric features has long been noted as a data quality
problem that affects the performance of software defect prediction (SDP) models. This …

Machine learning is an effective method for software defect prediction. The performance of
learning models can be affected by irrelative and redundant features. Feature selection …

In order to improve the effect of software defect prediction, many algorithms including feature
selection, have been proposed. Based on Wrapper and Filter hybrid framework, a feature …

Software defect prediction based on supervised learning plays a crucial role in guiding
software testing for resource allocation. In particular, it is worth noticing that using …