Software defect prediction based on supervised learning plays a crucial role in guiding
software testing for resource allocation. In particular, it is worth noticing that using …

Feature selection has become the essential step in many data mining applications. Using a
single feature subset selection method may generate local optima. Ensembles of feature …

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

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 …

Feature selection (FS) is a feasible solution for mitigating high dimensionality problem, and
many FS methods have been proposed in the context of software defect prediction (SDP) …

Defect prediction is an important task for preserving software quality. Most prior work on
defect prediction uses software features, such as the number of lines of code, to predict …

Automated software defect prediction is an important and fundamental activity in the domain
of software development. However, modern software systems are inherently large and …

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

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 …

BACKGROUND-Predicting defect-prone software components is an economically important
activity and so has received a good deal of attention. However, making sense of the many …