research-article

Supporting program comprehension with source code summarization

Authors:

Sonia Haiduc,

Jairo Aponte,

Andrian MarcusAuthors Info & Claims

ICSE '10: Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 2

Pages 223 - 226

https://doi.org/10.1145/1810295.1810335

Published: 01 May 2010 Publication History

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Abstract

One of the main challenges faced by today's developers is keeping up with the staggering amount of source code that needs to be read and understood. In order to help developers with this problem and reduce the costs associated with it, one solution is to use simple textual descriptions of source code entities that developers can grasp easily, while capturing the code semantics precisely. We propose an approach to automatically determine such descriptions, based on automated text summarization technology.

References

[1]

Corbi, T. A., "Program Understanding: Challenge For The 1990s", IBM Systems Journal, 28, 2, 1989, pp. 294--307.

Digital Library

Google Scholar

[2]

Kireyev, K., "Using Latent Semantic Analysis for Extractive Summarization", in Proc. of Text Analysis Conference, 2008.

Google Scholar

[3]

Ko, A. J., Myers, B. A., Coblenz, M. J., and Aung, H. H., "An Exploratory Study of How Developers Seek, Relate, and Collect Relevant Information during Software Maintenance Tasks", IEEE Transactions on Software Engineering, 32, 12, 2006, pp. 971--987.

Digital Library

Google Scholar

[4]

Kuhn, A., Ducasse, S., and Girba, T., "Semantic Clustering: Identifying Topics in Source Code", Information and Software Technology, 49, 3, 2007, pp. 230--243.

Digital Library

Google Scholar

[5]

Landauer, T., Foltz, P., and Laham, D., "An Introduction to Latent Semantic Analysis", Discourse Processes, 25, 2&3, 1998, pp. 259--284.

Crossref

Google Scholar

[6]

Murphy, G., Lightweight Structural Summarization as an Aid to Software Evolution, University of Washington, PhD Thesis, 1996.

Digital Library

Google Scholar

[7]

Nenkova, A. and Passonneau, R., "Evaluating Content Selection in Summarization: The Pyramid Method", in Proc. North American Chapter of the Assoc. for Computational Linguistics - Human Language Technologies, 2004.

Google Scholar

[8]

Poshyvanyk, D. and Marcus, A., "Combining Formal Concept Analysis with Information Retrieval for Concept Location in Source Code", in Proc. International Conference on Program Comprehension, 2007, pp. 37--46.

Digital Library

Google Scholar

[9]

Sparck-Jones, K., "Automatic summarising: The state of the art", Information Processing and Management: An International Journal, 43, 6, 2007, pp. 1449--1481.

Digital Library

Google Scholar

[10]

Starke, J., Luce, C., and Sillito, J., "Searching and Skimming: An Exploratory Study", in Proc. International Conference on Software Maintenance, 2009, pp. 157--166.

Crossref

Google Scholar

[11]

Steinberger, J. and Ježek, K., "Update Summarization Based on Latent Semantic Analysis", in Text, Speech and Dialogue, Springer Berlin / Heidelberg, 2009.

Digital Library

Google Scholar

[12]

Storey, M. A., Cheng, L. T., Bull, I., and Rigby, P., "Shared Waypoints and Social Tagging to Support Collaboration in Software Development", in Proc. Computer Supported Collaborative Work, 2006.

Digital Library

Google Scholar

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Index Terms

Supporting program comprehension with source code summarization
1. Software and its engineering
  1. Software notations and tools

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Published In

ICSE '10: Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 2

May 2010

554 pages

ISBN:9781605587196

DOI:10.1145/1810295

General Chairs:
Jeff Kramer
Imperial College, London, UK
,
Judith Bishop
Microsoft Research, Redmond
,
Program Chairs:
Prem Devanbu
University of California at Davis
,
Sebastian Uchitel
University of Buenos Aires, Argentina and Imperial College London, UK

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 01 May 2010

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SIGSOFT

ICSE '10: 32nd International Conference on Software Engineering

May 1 - 8, 2010

Cape Town, South Africa

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Overall Acceptance Rate 276 of 1,856 submissions, 15%

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Huang YHuang JChen XZheng Z(2025)Towards Improving the Performance of Comment Generation Models by Using Bytecode InformationIEEE Transactions on Software Engineering10.1109/TSE.2024.352371351:2(503-520)Online publication date: Feb-2025
https://doi.org/10.1109/TSE.2024.3523713
Fang HCai YTempero EKazman RTu YLefever JPisch E(2025)A Holistic Approach to Design Understanding Through Concept ExplanationIEEE Transactions on Software Engineering10.1109/TSE.2024.352297351:2(449-465)Online publication date: 1-Feb-2025
https://dl.acm.org/doi/10.1109/TSE.2024.3522973
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Abstract

References

Cited By

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Recommendations

CODES: mining source code descriptions from developers discussions

Improving automated source code summarization via an eye-tracking study of programmers

Supporting program comprehension by generating abstract code summary tree

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