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 …

Two primary issues have emerged in the machine learning and data mining community: how
to deal with imbalanced data and how to choose appropriate features. These are of …

Software defects may cause severe crashes in the system, leading to the software's high
maintenance costs. Early identification of these defects would lead to high-quality software …

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 …

Abstract Software Defect Prediction (SDP) is a significant and emerging concept in the field
of software development processes. Without an accurate model for predicting flaws in …

Improving software quality by proactively detecting potential defects during development is a
major goal of software engineering. Software defect prediction plays a central role in …

Abstract The performance of Software Defect Prediction (SDP) models depends on the
quality of dataset used for training the models. The high dimensionality of software metric …

Software Defect Prediction (SDP) models are built using software metrics derived from
software systems. The quality of SDP models depends largely on the quality of software …

In order to minimize the over-fitting and related factors that are caused by the high
dimensionality of the input data in software defect prediction, the attributes are often …