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

Software defect prediction is one of the most popular research topics in software
engineering. It aims to predict defect‐prone software modules before defects are discovered …

To improve software reliability, software defect prediction is utilized to assist developers in
finding potential bugs and allocating their testing efforts. Traditional defect prediction studies …

Software defect prediction, which predicts defective code regions, can help developers find
bugs and prioritize their testing efforts. To build accurate prediction models, previous studies …

Learning-based fault localization has been intensively studied recently. Prior studies have
shown that traditional Learning-to-Rank techniques can help precisely diagnose fault …

Software defect prediction, which predicts defective code regions, can assist developers in
finding bugs and prioritizing their testing efforts. Traditional defect prediction features often …

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 …

Highly imbalanced data typically make accurate predictions difficult. Unfortunately, software
defect datasets tend to have fewer defective modules than non-defective modules. Synthetic …

Defect prediction is a very meaningful topic, particularly at change-level. Change-level
defect prediction, which is also referred as just-in-time defect prediction, could not only …

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 …