Software defect prediction aims to find a reliable method for predicting defects in a particular
software project and assisting software engineers in allocating limited resources to release …

(1) Background: Code smell is the most popular and reliable method for detecting potential
errors in code. In real-world circumstances, a single source code may have multiple code …

Code smells are software flaws that make it challenging to comprehend, develop, and
maintain the software. Identifying and removing code smells is crucial for software quality …

Software quality is critical, as low quality, or “Code smell,” increases technical debt and
maintenance costs. There is a timely need for a collaborative model that detects and …

Code smell refers to substandard design patterns in software's source code that may lead to
faults-prone implementation. Machine learning-based code smell prediction models suffer …

Code smells indicate potential symptoms or problems in software due to inefficient design or
incomplete implementation. These problems can affect software quality in the long-term …

The advancement of machine learning (ML) models has led to the development of ML-
based approaches to improve numerous software engineering tasks in software …

Repairing code smells detected in the code or design of the system is one of the activities
contributing to increasing the software quality. In this study, we investigate the impact of non …

In modern times, refactoring is one of the significantly utilized approaches for enhancing the
software's quality like understandability, testability, and maintainability. Moreover, the …

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 …