research-article

Towards automatically generating summary comments for Java methods

Authors:

Giriprasad Sridhara,

Divya Muppaneni,

K. Vijay-ShankerAuthors Info & Claims

ASE '10: Proceedings of the 25th IEEE/ACM International Conference on Automated Software Engineering

Pages 43 - 52

https://doi.org/10.1145/1858996.1859006

Published: 20 September 2010 Publication History

Abstract

Studies have shown that good comments can help programmers quickly understand what a method does, aiding program comprehension and software maintenance. Unfortunately, few software projects adequately comment the code. One way to overcome the lack of human-written summary comments, and guard against obsolete comments, is to automatically generate them. In this paper, we present a novel technique to automatically generate descriptive summary comments for Java methods. Given the signature and body of a method, our automatic comment generator identifies the content for the summary and generates natural language text that summarizes the method's overall actions. According to programmers who judged our generated comments, the summaries are accurate, do not miss important content, and are reasonably concise.

References

[1]

}}T. Ball and J. R. Larus. Branch Prediction for Free. Conf on Programming Language Design and Implementation (PLDI), 1993.

Digital Library

[2]

}}D. Binkley, D. Lawrie, S. Maex, and C. Morrell. Impact of Limited Memory Resources. Intl Conf on Program Comprehension (ICPC), 2008.

Digital Library

[3]

}}R. P. Buse and W. R. Weimer. Automatic Documentation Inference for Exceptions. Intl Symp on Software Testing and Analysis (ISSTA), 2008.

Digital Library

[4]

}}P. P. Chang, S. A. Mahlke, and W. Hwu. Using Profile Information to Assist Classic Code Optimizations. Softw. Practice and Experience, 21(12):1301--1321, 1991.

Digital Library

[5]

}}M. E. Crosby, J. Scholtz, and S. Wiedenbeck. The Roles Beacons Play in Comprehension for Novice and Expert Programmers. Workshop of the Psychology of Programming Interest Group (PPIG), 2002.

[6]

}}S. C. B. de Souza, N. Anquetil, and K. M. de Oliveira. A Study of the Documentation Essential to Software Maintenance. Intl Conf on Design of Communication (SIGDOC), 2005.

Digital Library

[7]

}}E. Enslen, E. Hill, L. Pollock, and K. Vijay-Shanker. Mining Source Code to Automatically Split Identifiers for Software Analysis. Intl Working Conf on Mining Software Repositories (MSR), 2009.

Digital Library

[8]

}}T. E. Erickson. An Automated FORTRAN Documenter. Intl Conf on Systems Documentation (SIGDOC), 1982.

Digital Library

[9]

}}M. Fowler. Refactoring: Improving the Design of Existing Code. Addison-Wesley, 1999.

Digital Library

[10]

}}M. Harman, N. Gold, R. Hierons, and D. Binkley. Code Extraction Algorithms which Unify Slicing and Concept Assignment. Working Conf on Reverse Engineering (WCRE), 2002.

Digital Library

[11]

}}E. Hill. Integrating Natural Language and Program Structure Information to Improve Software Search and Exploration. PhD thesis, University of Delaware, 2010.

Digital Library

[12]

}}E. Hill, Z. P. Fry, H. Boyd, G. Sridhara, Y. Novikova, L. Pollock, and K. Vijay-Shanker. AMAP: Automatically Mining Abbreviation Expansions in Programs to Enhance Software Maintenance Tools. Intl Working Conf on Mining Software Repositories (MSR), 2008.

Digital Library

[13]

}}E. Hill, L. Pollock, and K. Vijay-Shanker. Automatically Capturing Source Code Context of NL-Queries for Software Maintenance and Reuse. Intl Conf on Software Engineering (ICSE), 2009.

Digital Library

[14]

}}M. Kajko-Mattsson. A Survey of Documentation Practice within Corrective Maintenance. Empirical Softw. Eng., 10(1):31--55, 2005.

Digital Library

[15]

}}D. E. Knuth. Literate Programming. The Computer Journal, 27(2), 1984.

Digital Library

[16]

}}B. Liblit, A. Begel, and E. Sweetser. Cognitive Perspectives on the Role of Naming in Computer Programs. Psychology of Programming Workshop (PPIG), 2006.

[17]

}}F. Long, X. Wang, and Y. Cai. API Hyperlinking via Structural Overlap. Foundations of Software Engineering (FSE), 2009.

Digital Library

[18]

}}I. Mani. Automatic Summarization. John Benjamins, 2001.

[19]

}}D. P. Marin. What Motivates Programmers to Comment? Master's thesis, University of California, Berkeley, 2005.

[20]

}}D. G. Novick and K. Ward. What Users Say They Want in Documentation. Intl Conf on Design of Communication (SIGDOC), 2006.

Digital Library

[21]

}}E. Reiter and R. Dale. Building Natural Language Generation Systems. Cambridge Univ. Press, 2000.

[22]

}}D. Roach, H. Berghel, and J. R. Talburt. An Interactive Source Commenter for Prolog Programs. SIGDOC Asterisk J. Comput. Doc., 14(4), 1990.

Digital Library

[23]

}}P. N. Robillard. Schematic Pseudocode for Program Constructs and its Computer Automation by SCHEMACODE. Commun. ACM, 29(11), 1986.

Digital Library

[24]

}}R. C. Seacord, D. Plakosh, and G. A. Lewis. Modernizing Legacy Systems: Software Technologies, Engineering Process and Business Practices. Addison-Wesley Longman Publishing Co., Inc., 2003.

Digital Library

[25]

}}M. J. Sousa. A Survey on the Software Maintenance Process. International Conference on Software Maintenance (ICSM), 1998.

Digital Library

[26]

}}G. Sridhara, E. Hill, L. Pollock, and K. Vijay-Shanker. Identifying Word Relations in Software: A Comparative Study of Semantic Similarity Tools. Intl Conf on Program Comprehension (ICPC), 2008.

Digital Library

[27]

}}M.-A. Storey. Theories, Methods and Tools in Program Comprehension: Past, Present and Future. Intl Workshop on Program Comprehension (IWPC), 2005.

Digital Library

[28]

}}SUN. Code Conventions for the Java Programming Language - Naming Conventions. online. http://java.sun.com/docs/codeconv/html/CodeConventions.doc8.html.

[29]

}}SUN. How to Write Doc Comments for the Javadoc Tool. online. http://java.sun.com/j2se/javadoc/writingdoccomments/.

[30]

}}A. A. Takang, P. A. Grubb, and R. D. Macredie. The Effects of Comments and Identifier Names on Program Comprehensibility: An Experimental Investigation. J. Prog. Lang., 4(3), 1996.

[31]

}}T. Tenny. Program Readability: Procedures Versus Comments. Trans. Softw. Eng., 14(9), 1988.

Digital Library

[32]

}}S. N. Woodfield, H. E. Dunsmore, and V. Y. Shen. The Effect of Modularization and Comments on Program Comprehension. Intl Conf on Software Engineering (ICSE), 1981.

Digital Library

Cited By

Kamran PDevanbu PStanford CFilkov VRay BZhou M(2024)Vision Paper: Proof-Carrying Code CompletionsProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering Workshops10.1145/3691621.3694932(35-42)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691621.3694932
Karas ZBansal AZhang YLi TMcMillan CHuang Y(2024)A Tale of Two Comprehensions? Analyzing Student Programmer Attention during Code SummarizationACM Transactions on Software Engineering and Methodology10.1145/366480833:7(1-37)Online publication date: 26-Aug-2024
https://dl.acm.org/doi/10.1145/3664808
Ding XPeng RChen XHuang YBian JZheng Z(2024)Do Code Summarization Models Process Too Much Information? Function Signature May Be All That Is NeededACM Transactions on Software Engineering and Methodology10.1145/365215633:6(1-35)Online publication date: 27-Jun-2024
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Index Terms

Towards automatically generating summary comments for Java methods
1. Software and its engineering
  1. Software creation and management
    1. Software post-development issues
      1. Documentation

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Information & Contributors

Information

Published In

cover image ACM Conferences

ASE '10: Proceedings of the 25th IEEE/ACM International Conference on Automated Software Engineering

September 2010

534 pages

ISBN:9781450301169

DOI:10.1145/1858996

General Chair:
Charles Pecheur
Université catholique de Louvain, Belgium
,
Program Chairs:
Jamie Andrews
University of Western Ontario, Canada
,
Elisabetta Di Nitto
Politecnico di Milano, Italy

Copyright © 2010 ACM.

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: 20 September 2010

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ASE10: IEEE/ACM International Conference on Automated Software Engineering

September 20 - 24, 2010

Antwerp, Belgium

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Overall Acceptance Rate 82 of 337 submissions, 24%

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Cited By

Kamran PDevanbu PStanford CFilkov VRay BZhou M(2024)Vision Paper: Proof-Carrying Code CompletionsProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering Workshops10.1145/3691621.3694932(35-42)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691621.3694932
Karas ZBansal AZhang YLi TMcMillan CHuang Y(2024)A Tale of Two Comprehensions? Analyzing Student Programmer Attention during Code SummarizationACM Transactions on Software Engineering and Methodology10.1145/366480833:7(1-37)Online publication date: 26-Aug-2024
https://dl.acm.org/doi/10.1145/3664808
Ding XPeng RChen XHuang YBian JZheng Z(2024)Do Code Summarization Models Process Too Much Information? Function Signature May Be All That Is NeededACM Transactions on Software Engineering and Methodology10.1145/365215633:6(1-35)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3652156
Tempero ETu Y(2024)Using Program Comprehension Models to Teach ComprehensibilityProceedings of the 26th Australasian Computing Education Conference10.1145/3636243.3636244(1-10)Online publication date: 29-Jan-2024
https://dl.acm.org/doi/10.1145/3636243.3636244
Sun WFang CChen YZhang QTao GYou YHan TGe YHu YLuo BChen Z(2024)An Extractive-and-Abstractive Framework for Source Code SummarizationACM Transactions on Software Engineering and Methodology10.1145/363274233:3(1-39)Online publication date: 14-Mar-2024
https://dl.acm.org/doi/10.1145/3632742
Zhu TLi ZPan MShi CZhang TPei YLi X(2024)Deep Is Better? An Empirical Comparison of Information Retrieval and Deep Learning Approaches to Code SummarizationACM Transactions on Software Engineering and Methodology10.1145/363197533:3(1-37)Online publication date: 15-Mar-2024
https://dl.acm.org/doi/10.1145/3631975
Hayatpur DHempel BChen KDuan WGuo PXia H(2024)Taking ASCII Drawings Seriously: How Programmers Diagram CodeProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642683(1-16)Online publication date: 11-May-2024
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Ahmed TPai KDevanbu PBarr ERoychoudhury APaiva AAbreu RStorey M(2024)Automatic Semantic Augmentation of Language Model Prompts (for Code Summarization)Proceedings of the IEEE/ACM 46th International Conference on Software Engineering10.1145/3597503.3639183(1-13)Online publication date: 20-May-2024
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Fang CSun WChen YChen XWei ZZhang QYou YLuo BLiu YChen Z(2024)Esale: Enhancing Code-Summary Alignment Learning for Source Code SummarizationIEEE Transactions on Software Engineering10.1109/TSE.2024.342227450:8(2077-2095)Online publication date: 1-Aug-2024
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Lin YWan CBai SGu X(2024)VarGAN: Adversarial Learning of Variable Semantic RepresentationsIEEE Transactions on Software Engineering10.1109/TSE.2024.339173050:6(1505-1517)Online publication date: 25-Apr-2024
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