Augmenting source code representation models with abstract syntax trees using tree traversal algorithms

Abstract

In an embodiment, a computer generates a multi-sequence vector that contains a plurality of distinct sequences of distinct nodes of a parse tree of source logic. Based on the multi-sequence vector, the computer trains a logic encoder. After training and in a production environment, the logic encoder infers a fixed-size encoded logic from new source logic. Based on the fixed-size encoded logic, the new source logic is detected as anomalous by an anomaly detector. Both of the logic encoder and the anomaly detector are machine learning models and, herein, they may be separately trained. In an embodiment, the logic encoder is based on a natural language processing (NLP) language model architecture such as bidirectional encoder representations from transformers (BERT), or novel training herein may be self-supervised according to skip-gram for use with an unlabeled training corpus.

Dependencies are inferred from the claim text.