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

To reduce technical debt and make code more maintainable, it is important to be able to
warn programmers about code smells. State-of-the-art code small detectors use deep …

Code smells regularly cause potential software quality problems in software development.
Thus, code smell detection has attracted the attention of many researchers. A number of …

Code smells are poor implementation choices that developers apply while evolving source
code and that affect program maintainability. Multiple automated code smell detectors have …

Context: A substantial amount of work has been done to detect smells in source code using
metrics-based and heuristics-based methods. Machine learning methods have been …

Developers continuously maintain software systems to adapt to new requirements and to fix
bugs. Due to the complexity of maintenance tasks and the time-to-market, developers make …

Code-smells are identified, in general, by using a set of detection rules. These rules are
manually defined to identify the key symptoms that characterize a code-smell using …

A smell in software refers to a symptom introduced in software artifacts such as architecture,
design or code. A code smell can potentially cause deeper and serious problems, while …

Context The code smell identification aims to reveal code structures that harm the software
maintainability. Such identification usually requires a deep understanding of multiple parts of …

Code smells can be subjectively interpreted, the results provided by detectors are usually
different, the agreement in the results is scarce, and a benchmark for the comparison of …