Quality Improvement in Annotated Code

Abstract

In order to customize their behavior at runtime, a wide sort of modern frameworks do use code annotations at the applications? classes as metadata configuration. However, despite its popularity, this type of metadata definition inserts complexity and semantic coupling that is ignored by traditional software metrics. This paper presents identified bad smells in annotated code and defines new metrics that help in their detection by enabling a quantitative assessment of complexity and coupling in this type of code. Moreover, it proposes some strategies to detect those bad smells by using the defined metrics and introduces an open-source tool created to automate the process of bad smell discovery on annotated code.