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 …

Context Automated classifiers, often based on machine learning (ML), are increasingly used
in software engineering (SE) for labelling previously unseen SE data. Researchers have …

Empirical studies on software effort estimation have employed hyper-parameter tuning
algorithms to improve model accuracy and stability. While these tuners can improve model …

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 …

Automatically generated static code warnings suffer from a large number of false alarms.
Hence, developers only take action on a small percent of those warnings. To better predict …

To reduce technical debt and make code more maintainable, it is important to be able to
warn programmers about code smells. State-of-the-art code small detectors use deep …

To preserve software quality and maintainability, machine learning-based code smell
detection has been proposed, and the results are promising. This research proposes an …

When data is scarce, software analytics can make many mistakes. For example, consider
learning predictors for open source project health (eg, the number of closed pull requests in …

Classification and regression trees (CART) have been reported to be competitive machine
learning algorithms for software effort estimation. In this work, we analyze the impact of …

Software developed on public platform is a source of data that can be used to make
predictions about those projects. While the individual developing activity may be random …