Enhancing Code Quality Through Static Analysis: Optimizing the Flava Tool for Detecting Code Smells and Errors Using SonarQube Integration

This article focuses on the optimization and enhancement of the Flava tool, with a specific emphasis on integrating static code analysis capabilities. The primary objective is to evaluate various approaches to addressing issues related to code quality and propose effective measures to mitigate common problems such as “code smells” and errors. By expanding the static analysis features of the Flava tool, this work enables the automated detection of code defects, thereby supporting the identification and correction of problematic areas in the code. As a result, the tool provides more comprehensive support for improving the quality and maintainability of software, ultimately leading to more efficient and sustainable software development processes. This research presents significant insights into improving software development workflows, increasing code reliability, and promoting best practices in software engineering. The enhanced Flava tool will contribute to better code quality and reduce the technical debt often associated with poor coding practices. Furthermore, this research addresses the growing need for tools that can seamlessly integrate into development environments, providing real-time feedback and aiding developers in proactively identifying potential issues before they become critical problems.