Systems and methods for code repository embedding for tagging and summarization tasks using attention on multiple code domains

A system, comprising:

• an electronic device comprising:
• a computer processor;
• and
• a memory storing a computer program comprising instructions;
• a database comprising a curated dataset of labelled data;
• and
• a code repository database comprising a plurality of scripts;
• wherein, when read and executed by the computer processor, the instructions cause the one computer processor to perform steps comprising:
• crawling the code repository database to identify a plurality of scripts in the code repository;
• generating docstring embeddings, code embeddings, and dependency embeddings from the plurality of scripts in the code repository database using one or more encoders;
• applying a machine learning algorithm to each of the docstring embeddings, the code embeddings, and the dependency embeddings, resulting in a plurality of code repository representations;
• concatenating the plurality of code repository representations;
• weighing the plurality of code repository representations using attention weights for an attention mechanism, resulting in an abstract vector, wherein the attention weights are trained from a curated dataset collected from a labelled dataset;
• tagging or summarizing the code repository based on the abstract vector;
• and
• classifying the code repository representations based on the tagging or summarizing.

A non-transitory computer readable storage medium, including instructions stored thereon, which when read and executed by one or more computer processors, cause the one or more computer processors to perform steps comprising:

• crawling a code repository database to identify a plurality of scripts in a code repository;
• generating docstring embeddings, code embeddings, and dependency embeddings from the plurality of scripts in the code repository
• applying a machine learning algorithm to each of the docstring embeddings, the code embeddings, and the dependency embeddings, resulting in a plurality of code repository representations;
• concatenating the plurality of code repository representations;
• weighting the plurality of code repository representations using attention weights for an attention mechanism, resulting in an abstract vector, wherein the attention weights are trained from a curated dataset collected from a labelled dataset;
• tagging or summarizing the code repository based on the abstract vector;
• and
• classifying the code repository representations based on the tagging or summarizing.