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

Machine learning (ML) techniques increase the effectiveness of software engineering (SE)
lifecycle activities. We systematically collected, quality-assessed, summarized, and …

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 …

Task prioritization is one of the most researched areas in software development. Given the
huge number of papers written on the topic, it might be challenging for IT practitioners …

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 …

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 …

Code smells are poorly designed code structures indicating that the code may need to be
refactored. Recognizing code smells in practice is complex, and researchers strive to …

Maintaining the consistency of engineering artifacts is a challenge faced by several
engineering companies. This is more evident when the engineering artifacts are created …

Context Code smell detection is the process of identifying poorly designed and implemented
code pieces. Machine learning-based approaches require enormous amounts of manually …

Code smells are structures in code that may indicate maintainability issues. They are
challenging to define, and software engineers detect them differently. Mitigation of this …