Code smells are indicators of potential problems in software. They tend to have a negative
impact on software quality. Several studies use machine learning techniques to detect bad …

Code smells can compromise software quality in the long term by inducing technical debt.
For this reason, many approaches aimed at identifying these design flaws have been …

Code smell detection is essential to improve software quality, enhancing software
maintainability, and decrease the risk of faults and failures in the software system. In this …

Context. Among the static analysis tools available, SonarQube is one of the most used.
SonarQube detects Technical Debt (TD) items—ie, violations of coding rules—and then …

Context: Companies frequently invest effort to remove technical issues believed to impact
software qualities, such as removing anti-patterns or coding styles violations. Objective: We …

Anti-patterns are poor solutions to recurring design problems. Several empirical studies
have highlighted their negative impact on program comprehension, maintainability, as well …

Self-adaptive systems dynamically change their structure and behavior in response to
changes in their execution environment to ensure the quality of the services they provide …

Context: Security is vital to software developed for commercial or personal use. Although
more organizations are realizing the importance of applying secure coding practices, in …

Machine learning (ML) has been widely used in the literature to automate software
engineering tasks. However, ML outcomes may be sensitive to randomization in data …

Technical Debt (TD) is a metaphor that refers to short-term solutions in software
development that may affect the software development life cycle cost. Researchers have …