DeepAnna: Deep Learning based Java Annotation Recommendation and Misuse Detection

Annotations have been widely used in Java programs to support additional compile-time, deployment-time, and runtime processing. Developers use annotations to delegate repetitive logics such as object initialization and request forwarding to compilers and runtime frameworks. Therefore, these annotations are important for the correct execution of programs. In practice, however, developers often find it hard to correctly use annotations and the misuse of annotations has led to real bugs in Java programs. In this paper, we conduct an empirical study on Stack Overflow questions to investigate the major development frameworks that are involved in questions about Java annotations and the main problems encountered by developers in the use of Java annotations. Based on the findings of the study, we propose DeepAnna, a deep learning based Java annotation recommendation and misuse detection approach. Based on a corpus of Java programs with intensive use of annotations, DeepAnna trains a deep learning based multi-label classification model by considering both the structural and textual contexts of source code. DeepAnna can recommend annotations at both class level and method level. Our evaluation with a large corpus of open-source Java projects shows that DeepAnna outperforms state-of-the-art text multi-label classification approaches in annotation recommendation and can effectively detect annotation misuses. Based on our analysis, we submit 85 bug-fixing pull requests for annotation misuses in open-source projects and 20 of them have been accepted and merged.