In 2006, Geoffrey Hinton proposed the concept of training “Deep Neural Networks (DNNs)”
and an improved model training method to break the bottleneck of neural network …

Researchers have recently achieved significant advances in deep learning techniques,
which in turn has substantially advanced other research disciplines, such as natural …

Context: Previous studies have shown that existing deep learning-based approaches can
significantly improve the performance of vulnerability detection. They represent code in …

The advancements in machine learning techniques have encouraged researchers to apply
these techniques to a myriad of software engineering tasks that use source code analysis …

Context: An excessive number of code smells make a software system hard to evolve and
maintain. Machine learning methods, in addition to metric-based and heuristic-based …

The presence of code smells will increase the risk of failure, make software difficult to
maintain, and introduce potential technique debt in the future. Although many deep-learning …

Measuring and evaluating source code similarity is a fundamental software engineering
activity that embraces a broad range of applications, including but not limited to code …

Code smells are structures in code that often harm its quality. Manually detecting code
smells is challenging, so researchers proposed many automatic detectors. Traditional code …

Context Bad smells negatively impact software quality metrics such as understandability,
reusability, and maintainability. Reduced costs and enhanced software quality can be …

The accuracy reported for code smell-detecting tools varies depending on the dataset used
to evaluate the tools. Our survey of 45 existing datasets reveals that the adequacy of a …