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Extracting code segments and their descriptions from research articles

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

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  • (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
  • (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
  • (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

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cover image ACM Conferences
MSR '17: Proceedings of the 14th International Conference on Mining Software Repositories
May 2017
567 pages
ISBN:9781538615447

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IEEE Press

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Published: 20 May 2017

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  1. code snippet description
  2. information extraction
  3. mining software repositories
  4. text analysis

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View all
  • (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
  • (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
  • (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

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