CONTEXT: Software metrics may be used in fault prediction models to improve software
quality by predicting fault location. OBJECTIVE: This paper aims to identify software metrics …

Abstract Context: Effort-Aware Defect Prediction (EADP) ranks software modules or changes
based on their predicted number of defects (ie, considering modules or changes as effort) or …

Most software defect prediction approaches are trained and applied on data from the same
project. However, often a new project does not have enough training data. Cross-project …

Unsupervised models do not require the defect data to build the prediction models and
hence incur a low building cost and gain a wide application range. Consequently, it would …

Reliably predicting software defects is one of the holy grails of software engineering.
Researchers have devised and implemented a plethora of defect/bug prediction approaches …

Software fault prediction (SFP) is typically used to predict faults in software components.
Machine learning techniques (eg, classification) are widely used to tackle this problem. With …

We propose a practical defect prediction approach for companies that do not track defect
related data. Specifically, we investigate the applicability of cross-company (CC) data for …

Background. Recent years have seen an increasing interest in cross-project defect
prediction (CPDP), which aims to apply defect prediction models built on source projects to a …

Defect prediction has been an active research area for over four decades. Despite
numerous studies on defect prediction, the potential value of defect prediction in practice …

Building quality software is expensive and software quality assurance (QA) budgets are
limited. Data miners can learn defect predictors from static code features which can be used …