Context: Several tertiary studies have criticized the reporting of software engineering
secondary studies. Objective: Our objective is to identify guidelines for reporting software …

Recent studies of software defect prediction typically produce datasets, methods and
frameworks which allow software engineers to focus on development activities in terms of …

Context: Automated software defect prediction (SDP) methods are increasingly applied,
often with the use of machine learning (ML) techniques. Yet, the existing ML-based …

The use of learning-based techniques to achieve automated software vulnerability detection
has been of longstanding interest within the software security domain. These data-driven …

As a new term in the financial industry, FinTech has become a popular term that describes
novel technologies adopted by the financial service institutions. This term covers a large …

In the research of mining software repositories, we need to label a large amount of data to
construct a predictive model. The correctness of the labels will affect the performance of a …

Defect prediction models help software quality assurance teams to allocate their limited
resources to the most defect-prone modules. Model validation techniques, such as-fold …

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 …

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

Software defect prediction is one of the most active research areas in software engineering.
We can build a prediction model with defect data collected from a software project and …