research-article

Learning to spot and refactor inconsistent method names

Authors:

Tegawendé F. Bissyandé,

Yves Le TraonAuthors Info & Claims

ICSE '19: Proceedings of the 41st International Conference on Software Engineering

Pages 1 - 12

https://doi.org/10.1109/ICSE.2019.00019

Published: 25 May 2019 Publication History

Abstract

To ensure code readability and facilitate software maintenance, program methods must be named properly. In particular, method names must be consistent with the corresponding method implementations. Debugging method names remains an important topic in the literature, where various approaches analyze commonalities among method names in a large dataset to detect inconsistent method names and suggest better ones. We note that the state-of-the-art does not analyze the implemented code itself to assess consistency. We thus propose a novel automated approach to debugging method names based on the analysis of consistency between method names and method code. The approach leverages deep feature representation techniques adapted to the nature of each artifact. Experimental results on over 2.1 million Java methods show that we can achieve up to 15 percentage points improvement over the state-of-the-art, establishing a record performance of 67.9% F1-measure in identifying inconsistent method names. We further demonstrate that our approach yields up to 25% accuracy in suggesting full names, while the state-of-the-art lags far behind at 1.1% accuracy. Finally, we report on our success in fixing 66 inconsistent method names in a live study on projects in the wild.

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Khan MElhussiny MTobin WGulzar M(2024)An Empirical Evaluation of Method Signature Similarity in Java CodebasesProceeding of the 2024 5th Asia Service Sciences and Software Engineering Conference10.1145/3702138.3702152(35-42)Online publication date: 11-Sep-2024
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cover image ACM Conferences

ICSE '19: Proceedings of the 41st International Conference on Software Engineering

May 2019

1318 pages

General Chair:
Joanne M. Atlee
University of Waterloo, Canada
,
Program Chairs:
Tevfik Bultan
University of California, Santa Barbara
,
Jon Whittle
Monash University, Australia

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SIGSOFT: ACM Special Interest Group on Software Engineering
IEEE-CS: Computer Society

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IEEE Press

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Published: 25 May 2019

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ICSE '19: 41st International Conference on Software Engineering

May 25 - 31, 2019

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

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https://dl.acm.org/doi/10.1145/3702138.3702152
Yang GZhou YYang WYue TChen XChen T(2024)How Important Are Good Method Names in Neural Code Generation? A Model Robustness PerspectiveACM Transactions on Software Engineering and Methodology10.1145/363001033:3(1-35)Online publication date: 14-Mar-2024
https://dl.acm.org/doi/10.1145/3630010
Dong CJiang YNiu NZhang YLiu HRoychoudhury APaiva AAbreu RStorey M(2024)Context-Aware Name Recommendation for Field RenamingProceedings of the IEEE/ACM 46th International Conference on Software Engineering10.1145/3597503.3639195(1-13)Online publication date: 20-May-2024
https://dl.acm.org/doi/10.1145/3597503.3639195
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
https://dl.acm.org/doi/10.1109/TSE.2024.3391730
Wang TZhang YJiang LTang YLi GLiu H(2024)Deep learning based identification of inconsistent method names: How far are we?Empirical Software Engineering10.1007/s10664-024-10592-z30:1Online publication date: 25-Nov-2024
https://dl.acm.org/doi/10.1007/s10664-024-10592-z
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
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Liu HWang YWei ZXu YWang JLi HJi RJust RFraser G(2023)RefBERT: A Two-Stage Pre-trained Framework for Automatic Rename RefactoringProceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3597926.3598092(740-752)Online publication date: 12-Jul-2023
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Wang SWen MLin BLiu YBissyandé TMao X(2023)Pre-implementation Method Name Prediction for Object-oriented ProgrammingACM Transactions on Software Engineering and Methodology10.1145/359720332:6(1-35)Online publication date: 29-Sep-2023
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Suneja SZhuang YZheng YLaredo JMorari AKhurana U(2023)Incorporating Signal Awareness in Source Code Modeling: An Application to Vulnerability DetectionACM Transactions on Software Engineering and Methodology10.1145/359720232:6(1-40)Online publication date: 29-Sep-2023
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Tian HLiu KLi YKaboré AKoyuncu AHabib ALi LWen JKlein JBissyandé T(2023)The Best of Both Worlds: Combining Learned Embeddings with Engineered Features for Accurate Prediction of Correct PatchesACM Transactions on Software Engineering and Methodology10.1145/357603932:4(1-34)Online publication date: 27-May-2023
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