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

Software development effort and cost estimation (SDECE) is one of the most important tasks
in the field of software engineering. A large number of research papers have been published …

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

Highly imbalanced data typically make accurate predictions difficult. Unfortunately, software
defect datasets tend to have fewer defective modules than non-defective modules. Synthetic …

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

Software systems have become larger and more complex than ever. Such characteristics
make it very challengeable to prevent software defects. Therefore, automatically predicting …

While deep learning is an exciting new technique, the benefits of this method need to be
assessed with respect to its computational cost. This is particularly important for deep …

Software effort estimation accuracy is a key factor in effective planning, controlling, and
delivering a successful software project within budget and schedule. The overestimation and …

Context Due to the complex nature of software development process, traditional parametric
models and statistical methods often appear to be inadequate to model the increasingly …

Although there has been substantial research in software analytics for effort estimation in
traditional software projects, little work has been done for estimation in agile projects …