Abstract: Systems and methods for counteracting data-skewness for locality sensitive hashing via feature selection and pruning are disclosed. In one embodiment, a method for feature selection for counteracting data skewness on locality sensitive hashing (LSH)-based search may include: (1) ingesting, by an ingestion computer program and from a plurality of data sources, data; (2) extracting, by the ingestion computer program, a plurality of features from the ingested data; (3) transforming, by the ingestion computer program, each of the plurality of features into a feature vector; (4) selecting, by the ingestion computer program, a subset of the plurality of features; and (5) for each selected feature vector: computing, by the ingestion computer program, a random hash function for the selected feature; and inserting, by the ingestion computer program, an output of the random hash function into a hash table with the selected feature.

Abstract: Systems and methods for automated code analysis and tagging are disclosed. In one embodiment, a method for automated code analysis and tagging may include: (1) receiving, by a code annotation computer program executed by a computer processor, a training code snippet from a training codebase; (2) parsing, by the code annotation computer program, the training code snippet into a data structure; (3) quantifying, by the code annotation computer program, the data structure, (4) parsing, by the code annotation computer program, a docstring associated with the training code snippet into a plurality of keywords; (5) quantifying, by the code annotation computer program, the plurality of keywords; and (6) training, by the code annotation computer program, a code annotation model based on a similarity between the quantified data structure and a smoothing parameter for a Dirichlet prior smoothing estimate.