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 poor code design or implementation that affect the code maintenance
process and reduce the software quality. Therefore, code smell detection is important in …

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 …

Abstract Unsupervised Effort-Aware Defect Prediction (EADP) uses unlabeled data to
construct a model and ranks software modules according to the software feature values. Xu …

Abstract Effort-Aware Defect Prediction (EADP) technique sorts software modules by the
defect density and aims to find more bugs when testing a certain number of Lines of Code …

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 …

Numerous DL-based approaches have garnered considerable attention in the field of
software Log Anomaly Detection. However, a practical challenge persists: the class …

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 …

Software aging refers to a problem of performance decay in long-running software systems.
This phenomenon is primarily attributed to the accumulation of run-time errors, commonly …

A code smell is a measurable indicator that highlights significant issues in the software
development process caused by inadequate programming practices. These are usually …