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Cited by 294

The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.

A Neural Model for Generating Natural Language Summaries of Program Subroutines

Open access
LeClair, Alexander, Jiang, Siyuan, McMillan, Collin·2019·2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)
DOI 10.1109/icse.2019.00087
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Abstract

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.

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Cited by

Metadata

Authors
LeClair, A., Jiang, S., & McMillan, C.
Venue
2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)
Publisher
IEEE
Published
2019-05
Pages
795-806
DOI
10.1109/icse.2019.00087
OpenAlex
W2914501196
Cited by
294
References
11
FWCI
33.15
Type
conference paper
Language
en
Open access
green
Licence

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