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

Code smells violate software development principles that make the software more prone to
errors and changes. Researchers have developed code smell detectors using manual and …

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 …

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 …

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

Abstract EXplainable Artificial Intelligence (XAI) aims at improving users' trust in black-boxed
models by explaining their predictions. However, XAI techniques produced unreasonable …

In the context of limited maintenance resources, predicting the severity of code smells is
more practically useful than simply detecting them. Fontana et al. first empirically …

Abstract Context: Feature Envy is a common code smell that occurs when a method heavily
relies on data or functionality from other classes. Detecting Feature Envy is essential for …

Abstract Code Smell Detection (CSD) plays a crucial role in improving software quality and
maintainability. And Deep Learning (DL) techniques have emerged as a promising …

Sentiment analysis has been used to study aspects of software engineering, such as issue
resolution, toxicity, and self-admitted technical debt. To address the peculiarities of software …