Since 2009, the deep learning revolution, which was triggered by the introduction of
ImageNet, has stimulated the synergy between Software Engineering (SE) and Machine …

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 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 …

Data-driven defect prediction has become increasingly important in software engineering
process. Since it is not uncommon that data from a software project is insufficient for training …

One truism of deep learning is that the automatic feature engineering (seen in the first layers
of those networks) excuses data scientists from performing tedious manual feature …

Studies in software effort estimation (SEE) have explored the use of hyper-parameter tuning
for machine learning algorithms (MLA) to improve the accuracy of effort estimates. In other …

The cost of software testing could be reduced if faulty entities were identified prior to the
testing phase, which is possible with software fault prediction (SFP). In most SFP models …

Code review is an effective quality assurance practice, yet can be time-consuming since
reviewers have to carefully review all new added lines in a patch. Our analysis shows that at …

Cross-Project Defect Prediction (CPDP), which borrows data from similar projects by
combining a transfer learner with a classifier, have emerged as a promising way to predict …

Standard automatic methods for recognizing problematic development commits can be
greatly improved via the incremental application of human+ artificial expertise. In this …