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

Background Software fault prediction is the process of developing models that can be used
by the software practitioners in the early phases of software development life cycle for …

Several prediction approaches are contained in the arena of software engineering such as
prediction of effort, security, quality, fault, cost, and re-usability. All these prediction …

Background: The accurate prediction of where faults are likely to occur in code can help
direct test effort, reduce costs, and improve the quality of software. Objective: We investigate …

To facilitate software testing, and save testing costs, a wide range of machine learning
methods have been studied to predict defects in software modules. Unfortunately, the …

Context: Software defect prediction (SDP) is an important challenge in the field of software
engineering, hence much research work has been conducted, most notably through the use …

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 …

CONTEXT: Software defect prediction studies usually built models using within-company
data, but very few focused on the prediction models trained with cross-company data. It is …

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 …