Background: Cross project defect prediction (CPDP) recently gained considerable attention,
yet there are no systematic efforts to analyse existing empirical evidence. Objective: To …

Software defect prediction is one of the most popular research topics in software
engineering. It aims to predict defect‐prone software modules before defects are discovered …

Context: Automated software defect prediction (SDP) methods are increasingly applied,
often with the use of machine learning (ML) techniques. Yet, the existing ML-based …

Defect prediction models help software quality assurance teams to allocate their limited
resources to the most defect-prone modules. Model validation techniques, such as-fold …

Software defect prediction is one of the most active research areas in software engineering.
We can build a prediction model with defect data collected from a software project and …

Defect prediction models-classifiers that identify defect-prone software modules-have
configurable parameters that control their characteristics (eg, the number of trees in a …

Defect models that are trained on class imbalanced datasets (ie, the proportion of defective
and clean modules is not equally represented) are highly susceptible to produce inaccurate …

Defect prediction models are classifiers that are trained to identify defect-prone software
modules. Such classifiers have configurable parameters that control their characteristics (eg …

Defect prediction models are a well-known technique for identifying defect-prone files or
packages such that practitioners can allocate their quality assurance efforts (eg, testing and …

Many software defect prediction approaches have been proposed and most are effective in
within-project prediction settings. However, for new projects or projects with limited training …