research-article

Extracting code segments and their descriptions from research articles

Authors:

Preetha Chatterjee,

Benjamin Gause,

Hunter Hedinger,

Lori PollockAuthors Info & Claims

MSR '17: Proceedings of the 14th International Conference on Mining Software Repositories

Pages 91 - 101

https://doi.org/10.1109/MSR.2017.10

Published: 20 May 2017 Publication History

Abstract

The availability of large corpora of online software-related documents today presents an opportunity to use machine learning to improve integrated development environments by first automatically collecting code examples along with associated descriptions. Digital libraries of computer science research and education conference and journal articles can be a rich source for code examples that are used to motivate or explain particular concepts or issues. Because they are used as examples in an article, these code examples are accompanied by descriptions of their functionality, properties, or other associated information expressed in natural language text. Identifying code segments in these documents is relatively straightforward, thus this paper tackles the problem of extracting the natural language text that is associated with each code segment in an article. We present and evaluate a set of heuristics that address the challenges of the text often not being colocated with the code segment as in developer communications such as online forums.

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Cited By

Zhong H(2022)Enriching Compiler Testing with Real Program from Bug ReportProceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering10.1145/3551349.3556894(1-12)Online publication date: 10-Oct-2022
https://dl.acm.org/doi/10.1145/3551349.3556894
Yang BLiping ZFengrong Z(2019)A Survey on Research of Code CommentProceedings of the 2019 3rd International Conference on Management Engineering, Software Engineering and Service Sciences10.1145/3312662.3312710(45-51)Online publication date: 12-Jan-2019
https://dl.acm.org/doi/10.1145/3312662.3312710
Silva RRoy CRahman MSchneider KPaixao Kde Almeida Maia MGuéhéneuc YKhomh FSarro F(2019)Recommending comprehensive solutions for programming tasks by mining crowd knowledgeProceedings of the 27th International Conference on Program Comprehension10.1109/ICPC.2019.00054(358-368)Online publication date: 25-May-2019
https://dl.acm.org/doi/10.1109/ICPC.2019.00054

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Information & Contributors

Information

Published In

cover image ACM Conferences

MSR '17: Proceedings of the 14th International Conference on Mining Software Repositories

May 2017

567 pages

ISBN:9781538615447

General Chair:
Jesus M. Gonzalez-Barahona
Universidad Rey Juan Carlos
,
Program Chairs:
Abram Hindle
University of Alberta
,
Lin Tan
University of Waterloo

Sponsors

SIGSOFT: ACM Special Interest Group on Software Engineering
IEEE-CS: Computer Society
SADIO: SADIO

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IEEE Press

Publication History

Published: 20 May 2017

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ICSE '17

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IEEE-CS
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ICSE '17: 39th International Conference on Software Engineering

May 20 - 28, 2017

Buenos Aires, Argentina

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ICSE 2025

2025 IEEE/ACM 46th International Conference on Software Engineering

April 26 - May 3, 2025

Ottawa , ON , Canada

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Cited By

Zhong H(2022)Enriching Compiler Testing with Real Program from Bug ReportProceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering10.1145/3551349.3556894(1-12)Online publication date: 10-Oct-2022
https://dl.acm.org/doi/10.1145/3551349.3556894
Yang BLiping ZFengrong Z(2019)A Survey on Research of Code CommentProceedings of the 2019 3rd International Conference on Management Engineering, Software Engineering and Service Sciences10.1145/3312662.3312710(45-51)Online publication date: 12-Jan-2019
https://dl.acm.org/doi/10.1145/3312662.3312710
Silva RRoy CRahman MSchneider KPaixao Kde Almeida Maia MGuéhéneuc YKhomh FSarro F(2019)Recommending comprehensive solutions for programming tasks by mining crowd knowledgeProceedings of the 27th International Conference on Program Comprehension10.1109/ICPC.2019.00054(358-368)Online publication date: 25-May-2019
https://dl.acm.org/doi/10.1109/ICPC.2019.00054

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