Abstract Multiple Classifier Systems (MCS) have been widely studied as an alternative for
increasing accuracy in pattern recognition. One of the most promising MCS approaches is …

Background: Code smells indicate suboptimal design or implementation choices in the
source code that often lead it to be more change-and fault-prone. Researchers defined …

Code smells are symptoms of poor design and implementation choices weighing heavily on
the quality of produced source code. During the last decades several code smell detection …

Defect prediction models focus on identifying defect-prone code elements, for example to
allow practitioners to allocate testing resources on specific subsystems and to provide …

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

Code smells can compromise software quality in the long term by inducing technical debt.
For this reason, many approaches aimed at identifying these design flaws have been …

Community smells represent sub-optimal conditions appearing within software development
communities (eg, non-communicating sub-teams, deviant contributors, etc.) that may lead to …

Bug Prediction is an activity aimed at identifying defect-prone source code entities that
allows developers to focus testing efforts on specific areas of software systems. Recently, the …

Predicting the defect-proneness of a module can reduce the time, effort, manpower, and
consequently the cost to develop a software project. Since the causes of software defects …

Stacked ensemble, which formulates an ensemble by using a meta-learner to combine
(stack) the predictions of multiple base classifiers, suffers from the problem of suboptimal …