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

Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …

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

In this paper, we address the problem of automatic repair of software vulnerabilities with
deep learning. The major problem with data-driven vulnerability repair is that the few …

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 …

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 …

Code smells are the result of not following software engineering principles during software
development, especially in the design and coding phase. It leads to low maintainability. To …

Supervised learning-based code smell detection has become one of the dominant
approaches to identify code smell. Existing works optimize the process of code smell …

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 …

Machine learning‐based code smell detection (CSD) has been demonstrated to be a
valuable approach for improving software quality and enabling developers to identify …