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

Arterial hypertension (AH) is a progressive issue that grows in importance with the increased
average age of the world population. The potential role of artificial intelligence (AI) in its …

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 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 …

Most software defect prediction approaches are trained and applied on data from the same
project. However, often a new project does not have enough training data. Cross-project …

Cross-Project Defect Prediction (CPDP) as a means to focus quality assurance of software
projects was under heavy investigation in recent years. However, within the current state-of …

Change-level defect prediction [5], aka, Just-In-Time (JIT) defect prediction [1], is an
alternative to module-level defect prediction that offers several advantages. First, since code …

Unlike traditional defect prediction models that identify defect-prone modules, Just-In-Time
(JIT) defect prediction models identify defect-inducing changes. As such, JIT defect models …