  Deep learning techniques applied to program analysis tasks such as code
classification, summarization, and bug detection have seen widespread interest.
Traditional approaches, however, treat programming source code as natural
language text, which may neglect significant structural or semantic details.
Additionally, most current methods of representing source code focus solely on
the code, without considering beneficial additional context. This paper
explores the integration of static analysis and additional context such as bug
reports and design patterns into source code representations for deep learning
models. We use the Abstract Syntax Tree-based Neural Network (ASTNN) method and
augment it with additional context information obtained from bug reports and
design patterns, creating an enriched source code representation that
significantly enhances the performance of common software engineering tasks
such as code classification and code clone detection. Utilizing existing
open-source code data, our approach improves the representation and processing
of source code, thereby improving task performance.
