Code refactor renaming recommender

A system for code refactor renaming, the system comprising:

• one or more computer processors;
• one or more computer readable storage media;
• and
• computer program instructions to:
• receive a source code dataset, wherein the dataset is comprised of a plurality of functions and a plurality of classes;
• identify function names and class names from the plurality of functions and the plurality of classes of the source code dataset;
• tokenize each of the identified function and identified class names;
• generate feature vectors for the source code of the plurality of functions and the plurality of classes;
• generate feature vectors for the docstrings associated with the plurality of functions and the plurality of classes;
• combine each feature vector of the docstrings with the corresponding feature vector of the source code in the at least one of the functions and the class;
• train a machine learning model through regression to map the combined feature vectors to the corresponding tokenized function names;
• receiving, by the one or more processors, a programming code with the same naming convention as the determined naming convention at the trained machine learning model;
• and
• generating, by the one or more processors, a name recommendation for one or more functions with the trained machine learning model.

A computer program product for code refactor renaming comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processors to perform a function, the function comprising:

• receive a source code dataset, wherein the dataset is comprised of a plurality of functions and a plurality of classes;
• identify function names and class names from the plurality of functions and the plurality of classes of the source code dataset;
• tokenize each of the identified function and class names;
• generate feature vectors for the source code of the plurality of functions and the plurality of classes;
• generate feature vectors for the docstrings associated with the plurality of functions and the plurality of classes;
• combine each feature vector of the docstrings with the corresponding feature vector of the source code in the at least one of the functions and the class;
• train a machine learning model through regression to map the combined feature vectors to the corresponding tokenized function names;
• receiving, by the one or more processors, a programming code with the same naming convention as the determined naming convention at the trained machine learning model;
• and
• generating, by the one or more processors, a name recommendation for one or more functions with the trained machine learning model.