AI Can Write Java 25 Right with SonarQube | AI Coding Assistant

The text discusses the challenges and solutions associated with AI-generated Java code, particularly in the context of the recent Java 25 release. It highlights how AI tools, trained on outdated preview features, often generate code that is syntactically correct but semantically flawed due to changes in the final APIs, such as ScopedValue and module imports. The text further explains that these issues can lead to runtime errors and subtle, hard-to-detect bugs, despite the code appearing fluent and well-constructed. SonarQube's static analysis is presented as a crucial tool for identifying these issues, as it is based on the finalized API contracts rather than outdated training data. The document also details specific Java Enhancement Proposals (JEPs) and the corresponding SonarQube rules designed to catch common AI-generated code errors, emphasizing the importance of code verification in bridging the gap between AI fluency and programming language correctness.