article

Toward mining "concept keywords" from identifiers in large software projects

Authors:

Katsuhiko GondowAuthors Info & Claims

ACM SIGSOFT Software Engineering Notes, Volume 30, Issue 4

Pages 1 - 5

https://doi.org/10.1145/1082983.1083151

Published: 17 May 2005 Publication History

Abstract

We propose the Concept Keyword Term Frequency/Inverse Document Frequency (ckTF/IDF) method as a novel technique to efficiency mine concept keywords from identifiers in large software projects. ckTF/IDF is suitable for mining concept keywords, since the ckTF/IDF is more lightweight than the TF/IDF method, and the ckTF/IDF's heuristics is tuned for identifiers in programs.We then experimentally apply the ckTF/IDF to our educational operating system udos, consisting of around 5,000 lines in C code, which produced promising results; the udos's source code was processed in 1.4 seconds with an accuracy of around 57%. This preliminary result suggests that our approach is useful for mining concept keywords from identifiers, although we need more research and experience.

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Cited By

Krasniqi RDo H(2023)Towards semantically enhanced detection of emerging quality-related concerns in source codeSoftware Quality Journal10.1007/s11219-023-09614-831:3(865-915)Online publication date: 17-Feb-2023
https://doi.org/10.1007/s11219-023-09614-8
Guo JLiu JLiu XWan YZhao YLi LLiu KKlein JBissyandé T(2023) PyScribe –Learning to describe python code Software: Practice and Experience10.1002/spe.329154:3(501-527)Online publication date: 9-Dec-2023
https://doi.org/10.1002/spe.3291
Allamanis MBarr EBird CSutton CCheung SOrso AStorey M(2014)Learning natural coding conventionsProceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering10.1145/2635868.2635883(281-293)Online publication date: 11-Nov-2014
https://dl.acm.org/doi/10.1145/2635868.2635883
Show More Cited By

Index Terms

Toward mining "concept keywords" from identifiers in large software projects
1. Applied computing
  1. Document management and text processing
2. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM SIGSOFT Software Engineering Notes

ACM SIGSOFT Software Engineering Notes Volume 30, Issue 4

July 2005

1514 pages

ISSN:0163-5948

DOI:10.1145/1082983

Issue’s Table of Contents

MSR '05: Proceedings of the 2005 international workshop on Mining software repositories
May 2005
109 pages
ISBN:1595931236
DOI:10.1145/1083142

Copyright © 2005 Copyright is held by the owner/author(s).

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 May 2005

Published in SIGSOFT Volume 30, Issue 4

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Cited By

Krasniqi RDo H(2023)Towards semantically enhanced detection of emerging quality-related concerns in source codeSoftware Quality Journal10.1007/s11219-023-09614-831:3(865-915)Online publication date: 17-Feb-2023
https://doi.org/10.1007/s11219-023-09614-8
Guo JLiu JLiu XWan YZhao YLi LLiu KKlein JBissyandé T(2023) PyScribe –Learning to describe python code Software: Practice and Experience10.1002/spe.329154:3(501-527)Online publication date: 9-Dec-2023
https://doi.org/10.1002/spe.3291
Allamanis MBarr EBird CSutton CCheung SOrso AStorey M(2014)Learning natural coding conventionsProceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering10.1145/2635868.2635883(281-293)Online publication date: 11-Nov-2014
https://dl.acm.org/doi/10.1145/2635868.2635883
Bhattacharjee MGhosh KBanerjee AChatterjee S(2021)Multilabel Sentiment Prediction by Addressing Imbalanced Class Problem Using OversamplingAdvances in Smart Communication Technology and Information Processing10.1007/978-981-15-9433-5_23(239-249)Online publication date: 16-Feb-2021
https://doi.org/10.1007/978-981-15-9433-5_23
Velasco AAponte J(2020)Automated Fine Grained Traceability Links Recovery between High Level Requirements and Source Code ImplementationsParadigmPlus10.55969/paradigmplus.v1n2a21:2(18-41)Online publication date: 19-Aug-2020
https://doi.org/10.55969/paradigmplus.v1n2a2
Lu JWei YSun XLi BWen WZhou C(2018)Interactive Query Reformulation for Source-Code Search With Word RelationsIEEE Access10.1109/ACCESS.2018.28839636(75660-75668)Online publication date: 2018
https://doi.org/10.1109/ACCESS.2018.2883963
Li GLiu HLiu QWu Y(2018)Lexical Similarity Between Argument and Parameter Names: An Empirical StudyIEEE Access10.1109/ACCESS.2018.28751256(58461-58481)Online publication date: 2018
https://doi.org/10.1109/ACCESS.2018.2875125
Wang XPollock LVijay-Shanker K(2017)Automatically generating natural language descriptions for object-related statement sequences2017 IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER)10.1109/SANER.2017.7884622(205-216)Online publication date: Feb-2017
https://doi.org/10.1109/SANER.2017.7884622
Lawrie DBinkley D(2015)Navigating source code with words2015 IEEE 15th International Working Conference on Source Code Analysis and Manipulation (SCAM)10.1109/SCAM.2015.7335403(71-80)Online publication date: Sep-2015
https://doi.org/10.1109/SCAM.2015.7335403
Wang XPollock LVijay-Shanker K(2015)Developing a model of loop actions by mining loop characteristics from a large code corpusProceedings of the 2015 IEEE International Conference on Software Maintenance and Evolution (ICSME)10.1109/ICSM.2015.7332451(51-60)Online publication date: 29-Sep-2015
https://dl.acm.org/doi/10.1109/ICSM.2015.7332451
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