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Automatically mining software-based, semantically-similar words from comment-code mappings

Authors:

Matthew J. Howard,

K. Vijay-ShankerAuthors Info & Claims

MSR '13: Proceedings of the 10th Working Conference on Mining Software Repositories

Pages 377 - 386

Published: 18 May 2013 Publication History

Abstract

Many software development and maintenance tools involve matching between natural language words in different software artifacts (e.g., traceability) or between queries submitted by a user and software artifacts (e.g., code search). Because different people likely created the queries and various artifacts, the effectiveness of these tools is often improved by expanding queries and adding related words to textual artifact representations. Synonyms are particularly useful to overcome the mismatch in vocabularies, as well as other word relations that indicate semantic similarity. However, experience shows that many words are semantically similar in computer science situations, but not in typical natural language documents. In this paper, we present an automatic technique to mine semantically similar words, particularly in the software context. We leverage the role of leading comments for methods and programmer conventions in writing them. Our evaluation of our mined related comment-code word mappings that do not already occur in WordNet are indeed viewed as computer science, semantically-similar word pairs in high proportions.

References

[1]

C. D. Manning, P. Raghavan, and H. Schuetze, Introduction to Information Retrieval. Cambridge University Press, 2008.

Digital Library

[2]

D. Shepherd, Z. P. Fry, E. Hill, L. Pollock, and K. Vijay-Shanker, “Using natural language program analysis to locate and understand actionoriented concerns,” in AOSD ’07: Proceedings of the 6th International Conference on Aspect-oriented Software Development, 2007.

Digital Library

[3]

C. Fellbaum, WordNet: An Electronic Lexical Database. MIT Press, 1998.

[4]

E. Hill, L. Pollock, and K. Vijay-Shanker, “Exploring the neighborhood with Dora to expedite software maintenance,” in 22nd IEEE International Conference on Automated Software Engineering (ASE), 2007.

Digital Library

[5]

M. P. Robillard, “Automatic generation of suggestions for program investigation,” in 13th ACM SIGSOFT International Symposium on Foundations of Software Engineering, 2005.

Digital Library

[6]

Z. M. Saul, V. Filkov, P. Devanbu, and C. Bird, “Recommending random walks,” in Proceedings of the European Software Engineering Conference, 2007.

Digital Library

[7]

J. Krinke, “Identifying similar code with program dependence graphs,” in Eighth Working Conference on Reverse Engineering (WCRE), 2001.

Digital Library

[8]

S. Bajracharya, T. Ngo, E. Linstead, Y. Dou, P. Rigor, P. Baldi, and C. Lopes, “Sourcerer: a search engine for open source code supporting structure-based search,” in OOPSLA: Companion to the 21st ACM SIGPLAN, 2006.

Digital Library

[9]

R. Holmes and G. C. Murphy, “Using structural context to recommend source code examples,” in 27th International Conference on Software Engineering, 2005.

Digital Library

[10]

J. Anvik, L. Hiew, and G. C. Murphy, “Who should fix this bug?” in ICSE ’06: Proceedings of the 28th Int. Conference on Software Engineering, 2006.

Digital Library

[11]

T. Apiwattanapong, A. Orso, and M. J. Harrold, “A differencing algorithm for object-oriented programs,” in 19th IEEE International Conference on Automated Software Engineering, 2004.

Digital Library

[12]

G. Sridhara, E. Hill, L. Pollock, and K. Vijay-Shanker, “Identifying word relations in software: A comparative study of semantic similarity tools,” in Proceedings of the 2008 The 16th IEEE International Conference on Program Comprehension. IEEE Computer Society, 2008, pp. 123–132.

Digital Library

[13]

A. Budanitsky and G. Hirst, “Evaluating WordNet-based Measures of Lexical Semantic Relatedness,” Computational Linguistics, vol. 32, no. 1, 2006.

Digital Library

[14]

D. Jurafsky and J. H. Martin, Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, Second Edition. Prentice Hall, 2008.

Digital Library

[15]

T. Pedersen, S. Banerjee, and S. Patwardhan, “Maximizing semantic relatedness to perform word sense disambiguation,” University of Minnesota, Duluth, Tech. Rep., 2005.

[16]

J. Yang and L. Tan, “Inferring semantically related words from software context,” in Proceedings of the Working Conference on Mining Software Repositories (MSR’12), June 2012.

[17]

G. Sridhara, “Automatic generation of descriptive summary comments for methods in object-oriented programs,” Ph.D. dissertation, University of Delaware, Jan 2012.

[18]

M.-A. Storey, J. Ryall, R. I. Bull, D. Myers, and J. Singer, “Todo or to bug: exploring how task annotations play a role in the work practices of software developers,” in Proceedings of the 30th international conference on Software engineering, ser. ICSE ’08, 2008.

Digital Library

[19]

K. Toutanova, D. Klein, C. Manning, and Y. Singer, “Feature-rich partof-speech tagging with a cyclic dependency network,” in Proceedings of HLT-NAACL 2003, 2003, pp. 252–259.

Digital Library

[20]

S. L. Abebe and P. Tonella, “Natural language parsing of program element names for concept extraction,” in Proceedings of the 2010 IEEE 18th International Conference on Program Comprehension, ser. ICPC ’10, 2010, pp. 156–159.

Digital Library

[21]

S. Gupta, K. Vijay-Shanker, and L. Pollock, “Part-of-speech tagging of method names,” U of Delaware, Tech. Rep. UD-CIS; 2013-002, February 2013.

[22]

E. Enslen, E. Hill, L. Pollock, and K. Vijay-Shanker, “Mining source code to automatically split identifiers for software analysis,” in 6th IEEE Working Conference on Mining Software Repositories (MSR), May 2009.

Digital Library

[23]

C. D. Manning and H. Schuetze, Foundations of Statistical Natural Language Processing. The MIT Press, 1999.

Digital Library

[24]

L. Tan, D. Yuan, G. Krishna, and Y. Zhou, “/*iComment: Bugs or bad comments?*/,” in SOSP ’07: Proceedings of twenty-first ACM SIGOPS symposium on Operating systems principles. ACM, 2007, pp. 145–158.

Digital Library

[25]

L. Tan, Y. Zhou, and Y. Padioleau, “aComment: mining annotations from comments and code to detect interrupt related concurrency bugs,” in 33rd International Conference on Software Engineering, 2011.

Digital Library

[26]

B. Fluri, M. Würsch, and H. C. Gall, “Do code and comments coevolve? on the relation between source code and comment changes,” in 14th Working Conference on Reverse Engineering (WCRE), 2007.

Digital Library

[27]

D. J. Lawrie, H. Feild, and D. Binkley, “Leveraged quality assessment using information retrieval techniques,” in Proceedings of the 14th IEEE International Conference on Program Comprehension (ICPC’06), 2006.

Digital Library

[28]

A. Marcus, A. Sergeyev, V. Rajlich, and J. I. Maletic, “An information retrieval approach to concept location in source code,” in 11th Working Conference on Reverse Engineering (WCRE’04), 2004.

Digital Library

Cited By

Guo ZLiu SLiu JLi YChen LLu HZhou Y(2021)How Far Have We Progressed in Identifying Self-admitted Technical Debts? A Comprehensive Empirical StudyACM Transactions on Software Engineering and Methodology10.1145/344724730:4(1-56)Online publication date: 23-Jul-2021
https://dl.acm.org/doi/10.1145/3447247
Cao KChen CBaltes STreude CChen X(2021)Automated Query Reformulation for Efficient Search based on Query Logs From Stack OverflowProceedings of the 43rd International Conference on Software Engineering10.1109/ICSE43902.2021.00116(1273-1285)Online publication date: 22-May-2021
https://dl.acm.org/doi/10.1109/ICSE43902.2021.00116
Lu YHsiao IHsiao SCunningham JMcCarthy KLynch GBrooks CFerguson RHoppe U(2019)Exploring Programming Semantic Analytics with Deep Learning ModelsProceedings of the 9th International Conference on Learning Analytics & Knowledge10.1145/3303772.3303823(155-159)Online publication date: 4-Mar-2019
https://dl.acm.org/doi/10.1145/3303772.3303823
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Index Terms

Automatically mining software-based, semantically-similar words from comment-code mappings
1. Information systems
  1. Information systems applications
    1. Data mining
2. Software and its engineering
  1. Software creation and management
    1. Software post-development issues
      1. Software version control
  2. Software notations and tools
    1. Software configuration management and version control systems

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Published In

cover image Guide Proceedings

MSR '13: Proceedings of the 10th Working Conference on Mining Software Repositories

May 2013

438 pages

ISBN:9781467329361

General Chair:
Thomas Zimmermann,
Program Chairs:
Massimiliano Di Penta,
Sunghun Kim

Publisher

IEEE Press

Publication History

Published: 18 May 2013

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Guo ZLiu SLiu JLi YChen LLu HZhou Y(2021)How Far Have We Progressed in Identifying Self-admitted Technical Debts? A Comprehensive Empirical StudyACM Transactions on Software Engineering and Methodology10.1145/344724730:4(1-56)Online publication date: 23-Jul-2021
https://dl.acm.org/doi/10.1145/3447247
Cao KChen CBaltes STreude CChen X(2021)Automated Query Reformulation for Efficient Search based on Query Logs From Stack OverflowProceedings of the 43rd International Conference on Software Engineering10.1109/ICSE43902.2021.00116(1273-1285)Online publication date: 22-May-2021
https://dl.acm.org/doi/10.1109/ICSE43902.2021.00116
Lu YHsiao IHsiao SCunningham JMcCarthy KLynch GBrooks CFerguson RHoppe U(2019)Exploring Programming Semantic Analytics with Deep Learning ModelsProceedings of the 9th International Conference on Learning Analytics & Knowledge10.1145/3303772.3303823(155-159)Online publication date: 4-Mar-2019
https://dl.acm.org/doi/10.1145/3303772.3303823
Malik RPatra JPradel MAtlee JBultan TWhittle J(2019)NL2TypeProceedings of the 41st International Conference on Software Engineering10.1109/ICSE.2019.00045(304-315)Online publication date: 25-May-2019
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Rahman MMussbacher GAtlee JBultan T(2019)Supporting code search with context-aware, analytics-driven, effective query reformulationProceedings of the 41st International Conference on Software Engineering: Companion Proceedings10.1109/ICSE-Companion.2019.00088(226-229)Online publication date: 25-May-2019
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Rahman MRoy CLo D(2019)Automatic query reformulation for code search using crowdsourced knowledgeEmpirical Software Engineering10.1007/s10664-018-9671-024:4(1869-1924)Online publication date: 1-Aug-2019
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Han XYu TLo DHuchard MKästner CFraser G(2018)PerfLearner: learning from bug reports to understand and generate performance test framesProceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering10.1145/3238147.3238204(17-28)Online publication date: 3-Sep-2018
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Lu YHsiao ILee DSastry NWeber I(2018)Modeling Semantics between Programming Codes and AnnotationsProceedings of the 29th on Hypertext and Social Media10.1145/3209542.3209578(101-105)Online publication date: 3-Jul-2018
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Li ZYin GWang TZhang YYu YWang H(2018)Correlation-based software search by leveraging software term databaseFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-017-6573-z12:5(923-938)Online publication date: 1-Oct-2018
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Rahman MRoy CRosu GDi Penta MNguyen T(2017)Improved query reformulation for concept location using CodeRank and document structuresProceedings of the 32nd IEEE/ACM International Conference on Automated Software Engineering10.5555/3155562.3155618(428-439)Online publication date: 30-Oct-2017
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