The timely release of defect-free software and the optimization of development costs depend
on efficient software bug triaging (SBT) techniques. SBT can also help in managing the vast …

Software vulnerabilities are prevalent in software systems, causing a variety of problems
including deadlock, information loss, or system failures. Thus, early predictions of software …

Abstract Context: Defect Number Prediction (DNP) models can offer more benefits than
classification-based defect prediction. Recently, many researchers proposed to employ …

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

A Just-In-Time (JIT) defect prediction model is a classifier to predict if a commit is defect-
introducing. Recently, CC2Vec-a deep learning approach for Just-In-Time defect prediction …

Software quality assurance efforts often focus on identifying defective code. To find likely
defective code early, change-level defect prediction-aka. Just-In-Time (JIT) defect prediction …

Pre-trained models like BERT have achieved strong improvements on many natural
language processing (NLP) tasks, showing their great generalizability. The success of pre …

Identifying security bug reports (SBRs) accurately from a bug repository can reduce a
software product's security risk. However, the class imbalance problem exists for SBR …

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 …