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 …

Context Software defect prediction (SDP) plays an important role in allocating testing
resources reasonably, reducing testing costs, and ensuring software quality. However …

Software defect prediction is important to identify defects in the early phases of software
development life cycle. This early identification and thereby removal of software defects is …

Code smells represent poor implementation choices performed by developers when
enhancing source code. Their negative impact on source code maintainability and …

In software fault prediction systems, there are many hindrances for detecting faulty modules,
such as missing values or samples, data redundancy, irrelevance features, and correlation …

As a widely known chronic disease, diabetes mellitus is called a silent killer. It makes the
body produce less insulin and causes increased blood sugar, which leads to many …

Just-in-Time Software Defect Prediction (JIT-SDP) is an SDP approach that makes defect
predictions at the software change level. Most existing JIT-SDP work assumes that the …

The performance of software defect prediction (SDP) models depend on the quality of
considered software features. Redundant features and irrelevant features may reduce the …

There is a severe class imbalance problem in defect datasets, with nondefective data
dominating the distribution, making it easy to generate inaccurate software defect prediction …

Software defect prediction provides actionable outputs to software teams while contributing
to industrial success. Empirical studies have been conducted on software defect prediction …