A Method of Automatically Generating Java Function Comments Based on Program Analysis

Abstract

The present invention is a method for automatically generating Java function annotations based on program analysis: first, collect a large number of open source projects on GitHub, extract Java function information and calling relationships based on program analysis technology, and form a training data set; Network technology has trained a function-to-comment translation model, which can automatically generate comments for functions, and the effect is obvious; it effectively solves the problems of scarcity, irregularity and inconsistency of existing function comments in the current project, and improves code readability and maintainability.

Description

A method for automatic generation of Java function annotations based on program analysis technical field [0001] The present invention belongs to the field of computer technology, especially the field of software technology. The present invention proposes a program-based Analyzed Java function annotation automatic generation method, which can automatically generate annotations for functions by building a recurrent neural network model. release. Effectively solve the scarcity, irregularity, inconsistency and other problems of existing function annotations in the current project, thereby improving the Code readability and maintainability. Background technique [0002] With the rapid development of Internet technology and the rapid changes in the software industry, more and more projects have begun to Use a distributed collaborative development model. Developers usually build their own branches, develop on their own branches, and follow Then merge the branch into the ...

AI Technical Summary

Problems solved by technology

At the same time, common problems in the process of distributed collaborative development are: when developers develop or review code, they often spend more time understanding the code written by other developers

However, the previous work did not make full use of the information contained in the project that is beneficial to annotation generation and the advantages of deep learning, resulting in certain defects in the accuracy, comprehensibility and fluency of the generated annotations.

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