Software defect prediction aims to identify the potential defects of new software modules in
advance by constructing an effective prediction model. However, the model performance is …

Programming is a vital skill in computer science and engineering-related disciplines.
However, developing source code is an error-prone task. Logical errors in code are …

In this paper, we investigate linear discriminant analysis (LDA) methods for multiclass
classification problems in hyperspectral imaging. We note that LDA does not consider …

Building secure software is difficult, time-consuming, and expensive. Prediction models that
identify vulnerability prone software components can be used to focus security efforts, thus …

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 …

Background. Solving the class-imbalance problem of within-project software defect
prediction (SDP) is an important research topic. Although some class-imbalance learning …

To design effective tools for detecting and recovering from software failures requires a deep
understanding of software bug characteristics. We study software bug characteristics by …

Defect prediction on new projects or projects with limited historical data is an interesting
problem in software engineering. This is largely because it is difficult to collect defect …

Classifier specific (CS) and classifier agnostic (CA) feature importance methods are widely
used (often interchangeably) by prior studies to derive feature importance ranks from a …

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 …