Defect prediction models are a well-known technique for identifying defect-prone files or
packages such that practitioners can allocate their quality assurance efforts (eg, testing and …

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 …

Purpose Previous studies that dealt with corporate governance have witnessed gradually
significant growth that created some new trends. The purpose of this paper is to be involved …

Software code review, ie, the practice of having third-party team members critique changes
to a software system, is a well-established best practice in both open source and proprietary …

Researchers in software engineering have attempted to improve software development by
mining and analyzing software repositories. Since the majority of the software engineering …

There has been a great deal of interest in defect prediction: using prediction models trained
on historical data to help focus quality-control resources in ongoing development. Since …

Machine learning classifiers have recently emerged as a way to predict the introduction of
bugs in changes made to source code files. The classifier is first trained on software history …

With the rise of the Mining Software Repositories (MSR) field, defect datasets extracted from
software repositories play a foundational role in many empirical studies related to software …

Just-in-Time (JIT) defect prediction-a technique which aims to predict bugs at change level-
has been paid more attention. JIT defect prediction leverages the SZZ approach to identify …

Human Affectiveness, ie, The emotional state of a person, plays a crucial role in many
domains where it can make or break a team's ability to produce successful products …