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 …

Abstract Effort‐Aware Defect Prediction (EADP) methods sort software modules based on
the defect density and guide the testing team to inspect the modules with high defect density …

Effort‐aware cross‐project defect prediction (EACPDP), which uses cross‐project software
modules to build a model to rank within‐project software modules based on the defect …

Software defect prediction (SDP) plays a significant role in detecting the most likely defective
software modules and optimizing the allocation of testing resources. In practice, though …

This paper aims to improve software fault-proneness prediction by investigating the
unexplored effects on classification performance of the temporal decisions made by …

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 …

Abstract Continuous Build Outcome Prediction (CBOP) is a lightweight implementation of
Continuous Defect Prediction (CDP). CBOP combines: 1) results of continuous integration …

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 …

Software fault-proneness prediction is an active research area, with many factors affecting
prediction performance extensively studied. However, the impact of the learning approach …

Abstract Model instability refers to where a machine learning model trained on historical
data becomes less reliable over time due to Concept Drift (CD). CD refers to the …