This study presented an analysis of code quality in open-source projects, an essential topic for understanding system maintainability and evolution. It consisted of an applied research study, with a mixed-methods approach, of a descriptive and exploratory nature, whose objective was to identify the main quality issues present in publicly accessible software repositories. A total of 122 projects developed in Java and Python were examined, using static analysis tools such as SonarQube, PMD, and Prospector, considering metrics such as code smells, defects, code duplication, technical debt, and automated quality assessments. The results revealed relevant differences between the ecosystems, with Java presenting greater internal variability and a higher average density of issues, while Python demonstrated lower averages but greater asymmetry in metrics such as technical debt and reliability. The analysis revealed a strong relationship between code smells, defects, and technical debt, indicating that the accumulation of inadequate practices increases maintenance effort, whereas external factors, such as project size and number of contributors, had limited impact. It was concluded that internal code quality depends mainly on the practices adopted by developers and that static analysis constitutes a fundamental resource to support maintenance, standardization, and system evolution in collaborative environments.