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

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

To improve software reliability, software defect prediction is utilized to assist developers in
finding potential bugs and allocating their testing efforts. Traditional defect prediction studies …

Software defect prediction, which predicts defective code regions, can help developers find
bugs and prioritize their testing efforts. To build accurate prediction models, previous studies …

Feature engineering plays a vital role in big data analytics. Machine learning and data
mining algorithms cannot work without data. Little can be achieved if there are few features …

Software defect prediction, which predicts defective code regions, can assist developers in
finding bugs and prioritizing their testing efforts. Traditional defect prediction features often …

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 …

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 …

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 …

Defect prediction models help software quality assurance teams to effectively allocate their
limited resources to the most defect-prone software modules. A variety of classification …