Systems and method for automated code analysis and tagging

A method for automated code analysis and tagging, comprising:

• receiving, by a code annotation computer program executed by a computer processor, a training code snippet from a training codebase;
• parsing, by the code annotation computer program, the training code snippet into a data structure;
• quantifying, by the code annotation computer program, the data structure,
• parsing, by the code annotation computer program, a docstring associated with the training code snippet into a plurality of keywords;
• quantifying, by the code annotation computer program, the plurality of keywords;
• and
• tuning, by the code annotation computer program, a bandwidth of a Gaussian kernel to compute a similarity between the quantified data structures and a smoothing parameter for a Dirichlet prior smoothing estimate,
• wherein annotations for the training code snippet is generated by matrix multiplication of a test code-training code probability matrix and a training code-word probability matrix, wherein the test code-training code probability matrix is computed from a Gaussian kernel density estimate between each test code snippet and each training code snippet.

A method for automated code analysis and tagging, comprising:

• parsing, by a code annotation computer program executed by a computer processor, an unannotated code snippet into a data structure;
• quantifying, by the code annotation computer program, the data structure;
• quantifying, by the code annotation computer program, keywords for a plurality of training code snippets by matrix multiplication of a test code-training code probability matrix and a training code-word probability matrix, wherein the test code-training code probability matrix is computed from a Gaussian kernel density estimate between each test code snippet and each of the plurality of training code snippets;
• computing, by the code annotation computer program, conditional probabilities of the unannotated code snippet occurring in each of the plurality of training code snippets;
• computing, by the code annotation computer program, a conditional probability of each of the keywords in the training code snippets for the unannotated code snippet;
• selecting, by the code annotation computer program, a plurality of the keywords based on the conditional probability for each keyword;
• and
• annotating, by the code annotation computer program, the unannotated code snippet with the selected keywords.