Software maintenance

Operating system

Source text

Heuristics

Internal documentation

Software system

Code refactoring

Identifier

Artificial intelligence

Automatic summarization

Set (abstract data type)

Software construction

Natural language

Programming language

Machine translation

Natural language processing

Code (set theory)

Program comprehension

Software

Computer science

Source code

Java

Documentation

Subroutine

Process (computing)

Source code summarization -- creating natural language descriptions of source code behavior -- is a rapidly-growing research topic with applications to automatic documentation generation, program comprehension, and software maintenance. Traditional techniques relied on heuristics and templates built manually by human experts. Recently, data-driven approaches based on neural machine translation have largely overtaken template-based systems. But nearly all of these techniques rely almost entirely on programs having good internal documentation; without clear identifier names, the models fail to create good summaries. In this paper, we present a neural model that combines words from code with code structure from an AST. Unlike previous approaches, our model processes each data source as a separate input, which allows the model to learn code structure independent of the text in code. This process helps our approach provide coherent summaries in many cases even when zero internal documentation is provided. We evaluate our technique with a dataset we created from 2.1m Java methods. We find improvement over two baseline techniques from SE literature and one from NLP literature.

A Neural Model for Generating Natural Language Summaries of Program Subroutines