Artificial intelligence (AI) has been successfully applied in industry for decades, ranging from
the emergence of expert systems in the 1960s to the wide popularity of deep learning today …

Fixing software bugs can be colossally expensive, especially if they are discovered in the
later phases of the software development life cycle. As such, bug prediction has been a …

Software Defect Prediction (SDP) is crucial towards software quality assurance in software
engineering. SDP analyzes the software metrics data for timely prediction of defect prone …

Abstract Just-In-Time Software Defect Prediction (JIT-SDP) is concerned with predicting
whether software changes are defect-inducing or clean. It operates in scenarios where …

Inter-release defect prediction (IRDP) is a practical scenario that employs the datasets of the
previous release to build a prediction model and predicts defects for the current release …

The performance of software defect prediction (SDP) models is known to be dependent on
the datasets used for training the models. Evolving data in a dynamic software development …

Concept drift (CD) refers to data distributions that may vary after a minimum stable period.
CD negatively influences models' performance of software defect prediction (SDP) trained …

Concept drift (CD) refers to a phenomenon where the data distribution within datasets
changes over time, and this can have adverse effects on the performance of prediction …

In cross-version defect prediction (CVDP), historical data is derived from the prior version of
the same project to predict defects of the current version. Recent studies in CVDP focus on …

The early and accurate prediction of defects helps in testing software and therefore leads to
an overall higher-quality product. Due to drift in software defect data, prediction model …