Abstract Software Defect Prediction (SDP) is crucial for enhancing software quality and
minimizing issues after release. The advent of machine learning, particularly in Cross …

Software defect prediction is a crucial area of study focused on enhancing software quality
and cutting down on software upkeep expenses. Cross Project Defect Prediction (CPDP) is …

Software defect prediction has attracted much attention of researchers in software
engineering. At present, feature selection approaches have been introduced into software …

Cross-project defect prediction (CPDP) involves the use of other projects (aka source
projects) for training and persuasive model building for a particular project (aka target …

Background: Software defect prediction models aim at identifying the potential faulty
modules of a software project based on historical data collected from previous versions of …

Cross-project defect prediction (CPDP) uses the labeled data from external source software
projects to compensate the shortage of useful data in the target project, in order to build a …

Recently, most studies have considered the default value for hyper parameters of the
classification methods used by cross-project defect prediction (CPDP) methods. However, in …

Using classification methods to predict software defect is receiving a great deal of attention
and most of the existing studies primarily conduct prediction under the within‐project setting …

Software defect prediction (SDP) technology is receiving widely attention and most of SDP
models are trained on data from the same project. However, at an early phase of the …

Heterogeneous cross-project defect prediction (HCPDP) aims to predict defects in a target
project with limited historical defect data via a defect prediction (DP) model trained with …