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 …

Defect prediction models are a well-known technique for identifying defect-prone files or
packages such that practitioners can allocate their quality assurance efforts (eg, testing and …

Existing work on software patches often use features specific to a single task. These works
often rely on manually identified features, and human effort is required to identify these …

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 …

This paper presents an approach based on machine learning to predict which components
of a software application contain security vulnerabilities. The approach is based on text …

Many software defect prediction models have been built using historical defect data
obtained by mining software repositories (MSR). Recent studies have discovered that data …

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 …

As software systems play an increasingly important role in our lives, their complexity
continues to increase. The increased complexity of software systems makes the assurance …

Bug-tracking and issue-tracking systems tend to be populated with bugs, issues, or tickets
written by a wide variety of bug reporters, with different levels of training and knowledge …

Traditional just-in-time defect prediction approaches have been using changed lines of
software to predict defective-changes in software development. However, they disregard …