Recent years have experienced sustained focus in research on software defect prediction
that aims to predict the likelihood of software defects. Moreover, with the increased interest …

Software development is a very broad activity that captures the entire life cycle of a software,
which includes designing, programming, maintenance and so on. In this study, we focus on …

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

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 …

Software systems have become larger and more complex than ever. Such characteristics
make it very challengeable to prevent software defects. Therefore, automatically predicting …

Defect prediction aims to automatically identify potential defective code with minimal human
intervention and has been widely studied in the literature. Just-in-Time (JIT) defect prediction …

Defect prediction is proposed to assist practitioners effectively prioritize limited Software
Quality Assurance (SQA) resources on the most risky files that are likely to have post-release …

Defect prediction models are proposed to help a team prioritize the areas of source code
files that need Software Quality Assurance (SQA) based on the likelihood of having defects …

Context: Generally, there are more non-defective instances than defective instances in the
datasets used for software defect prediction (SDP), which is referred to as the class …

To improve software quality, just-in-time defect prediction (JIT-DP)(identifying defect-
inducing commits) and just-in-time defect localization (JIT-DL)(identifying defect-inducing …