research-article

Automatically detecting and describing high level actions within methods

Authors:

Giriprasad Sridhara,

K. Vijay-ShankerAuthors Info & Claims

ICSE '11: Proceedings of the 33rd International Conference on Software Engineering

Pages 101 - 110

https://doi.org/10.1145/1985793.1985808

Published: 21 May 2011 Publication History

Abstract

One approach to easing program comprehension is to reduce the amount of code that a developer has to read. Describing the high level abstract algorithmic actions associated with code fragments using succinct natural language phrases potentially enables a newcomer to focus on fewer and more abstract concepts when trying to understand a given method. Unfortunately, such descriptions are typically missing because it is tedious to create them manually.

We present an automatic technique for identifying code fragments that implement high level abstractions of actions and expressing them as a natural language description. Our studies of 1000 Java programs indicate that our heuristics for identifying code fragments implementing high level actions are widely applicable. Judgements of our generated descriptions by 15 experienced Java programmers strongly suggest that indeed they view the fragments that we identify as representing high level actions and our synthesized descriptions accurately express the abstraction.

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Cited By

Bibi NMaqbool ARana TAfzal FKhan A(2024)C2B: A Semantic Source Code Retrieval Model Using CodeT5 and Bi-LSTMApplied Sciences10.3390/app1413579514:13(5795)Online publication date: 2-Jul-2024
https://doi.org/10.3390/app14135795
Mastropaolo ACiniselli MPascarella LTufano RAghajani EBavota GBaysal OLinares-Vasquez MMoran KSteinmacher I(2024)Towards Summarizing Code Snippets Using Pre-Trained TransformersProceedings of the 32nd IEEE/ACM International Conference on Program Comprehension10.1145/3643916.3644400(1-12)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3643916.3644400
Mastropaolo ACiniselli MDi Penta MBavota GRoychoudhury APaiva AAbreu RStorey M(2024)Evaluating Code Summarization Techniques: A New Metric and an Empirical CharacterizationProceedings of the IEEE/ACM 46th International Conference on Software Engineering10.1145/3597503.3639174(1-13)Online publication date: 20-May-2024
https://dl.acm.org/doi/10.1145/3597503.3639174
Show More Cited By

Index Terms

Automatically detecting and describing high level actions within methods
1. Software and its engineering
  1. Software creation and management
    1. Software post-development issues
      1. Documentation

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Published In

cover image ACM Conferences

ICSE '11: Proceedings of the 33rd International Conference on Software Engineering

May 2011

1258 pages

ISBN:9781450304450

DOI:10.1145/1985793

General Chair:
Richard N. Taylor
UC Irvine, USA
,
Program Chairs:
Harald Gall
University of Zurich, Switzerland
,
Nenad Medvidović
University of Southern California, USA

Copyright © 2011 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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Publication History

Published: 21 May 2011

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ICSE11: International Conference on Software Engineering

May 21 - 28, 2011

HI, Waikiki, Honolulu, USA

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

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Cited By

Bibi NMaqbool ARana TAfzal FKhan A(2024)C2B: A Semantic Source Code Retrieval Model Using CodeT5 and Bi-LSTMApplied Sciences10.3390/app1413579514:13(5795)Online publication date: 2-Jul-2024
https://doi.org/10.3390/app14135795
Mastropaolo ACiniselli MPascarella LTufano RAghajani EBavota GBaysal OLinares-Vasquez MMoran KSteinmacher I(2024)Towards Summarizing Code Snippets Using Pre-Trained TransformersProceedings of the 32nd IEEE/ACM International Conference on Program Comprehension10.1145/3643916.3644400(1-12)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3643916.3644400
Mastropaolo ACiniselli MDi Penta MBavota GRoychoudhury APaiva AAbreu RStorey M(2024)Evaluating Code Summarization Techniques: A New Metric and an Empirical CharacterizationProceedings of the IEEE/ACM 46th International Conference on Software Engineering10.1145/3597503.3639174(1-13)Online publication date: 20-May-2024
https://dl.acm.org/doi/10.1145/3597503.3639174
Liang HHuang C(2024)Integrating Non-Fourier and AST-Structural Relative Position Representations Into Transformer-Based Model for Source Code SummarizationIEEE Access10.1109/ACCESS.2024.335439012(9871-9889)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3354390
Guo HChen XHuang YWang YDing XZheng ZZhou XDai H(2023)Snippet Comment Generation Based on Code Context ExpansionACM Transactions on Software Engineering and Methodology10.1145/361166433:1(1-30)Online publication date: 23-Nov-2023
https://dl.acm.org/doi/10.1145/3611664
Shi CZhu TZhang TPang JPan M(2023)Structural-semantics Guided Program Simplification for Understanding Neural Code Intelligence ModelsProceedings of the 14th Asia-Pacific Symposium on Internetware10.1145/3609437.3609438(1-11)Online publication date: 4-Aug-2023
https://dl.acm.org/doi/10.1145/3609437.3609438
Zhang JLuo JLiang JGong LHuang Z(2023)An Accurate Identifier Renaming Prediction and Suggestion ApproachACM Transactions on Software Engineering and Methodology10.1145/360310932:6(1-51)Online publication date: 29-Sep-2023
https://dl.acm.org/doi/10.1145/3603109
Huang YGuo HDing XShu JChen XLuo XZheng ZZhou X(2023)A Comparative Study on Method Comment and Inline CommentACM Transactions on Software Engineering and Methodology10.1145/358257032:5(1-26)Online publication date: 22-Jul-2023
https://dl.acm.org/doi/10.1145/3582570
Zhang MZhou GYu WHuang NLiu W(2023)GA-SCS: Graph-Augmented Source Code SummarizationACM Transactions on Asian and Low-Resource Language Information Processing10.1145/355482022:2(1-19)Online publication date: 21-Feb-2023
https://dl.acm.org/doi/10.1145/3554820
Wang CPeng XXing ZMeng X(2023)Beyond Literal Meaning: Uncover and Explain Implicit Knowledge in Code Through Wikipedia-Based Concept LinkingIEEE Transactions on Software Engineering10.1109/TSE.2023.325002949:5(3226-3240)Online publication date: 1-May-2023
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