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 …

Data preprocessing remains an important step in machine learning studies. This is because
proper preprocessing of imbalanced data can enable researchers to reduce defects as …

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 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 …

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 …

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

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 …

Software analytics have empowered software organisations to support a wide range of
improved decision-making and policy-making. However, such predictions made by software …

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