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 …

Recent studies of software defect prediction typically produce datasets, methods and
frameworks which allow software engineers to focus on development activities in terms of …

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 …

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 …

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

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 …

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

Several RGB-D datasets have been publicized over the past few years for facilitating
research in computer vision and robotics. However, the lack of comprehensive and fine …

Software fault localization, the act of identifying the locations of faults in a program, is widely
recognized to be one of the most tedious, time consuming, and expensive-yet equally critical …

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 …