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Comparing Word-Based and AST-Based Models for Design Pattern Recognition

Authors:

Sivajeet Chand,

Sushant Kumar Pandey,

Jennifer Horkoff,

Miroslaw Staron,

Miroslaw Ochodek,

Darko DurisicAuthors Info & Claims

PROMISE 2023: Proceedings of the 19th International Conference on Predictive Models and Data Analytics in Software Engineering

Pages 44 - 48

https://doi.org/10.1145/3617555.3617873

Published: 08 December 2023 Publication History

Abstract

Design patterns (DPs) provide reusable and general solutions for frequently encountered problems. Patterns are important to maintain the structure and quality of software products, in particular in large and distributed systems like automotive software. Modern language models (like Code2Vec or Word2Vec) indicate a deep understanding of programs, which has been shown to help in such tasks as program repair or program comprehension, and therefore show promise for DPR in industrial contexts. The models are trained in a self-supervised manner, using a large unlabelled code base, which allows them to quantify such abstract concepts as programming styles, coding guidelines, and, to some extent, the semantics of programs. This study demonstrates how two language models—Code2Vec and Word2Vec, trained on two public automotive repositories, can show the separation of programs containing specific DPs. The results show that the Code2Vec and Word2Vec produce average F1-scores of 0.781 and 0.690 on open-source Java programs, showing promise for DPR in practice.

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Cited By

Azeez HSharbaf MZamani BKolahdouz-Rahimi S(2024)Applying Pattern Language to Enhance IIoT System Design and Integration: From Theory to PracticeInformation10.3390/info1510059515:10(595)Online publication date: 30-Sep-2024
https://doi.org/10.3390/info15100595
Dey TLoungani JIvers JShang WLamothe MWan Z(2024)Smarter Project Selection for Software Engineering ResearchProceedings of the 20th International Conference on Predictive Models and Data Analytics in Software Engineering10.1145/3663533.3664037(12-21)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3663533.3664037

Index Terms

Comparing Word-Based and AST-Based Models for Design Pattern Recognition
1. Software and its engineering
  1. Software creation and management
    1. Designing software
      1. Software implementation planning

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Published In

cover image ACM Conferences

PROMISE 2023: Proceedings of the 19th International Conference on Predictive Models and Data Analytics in Software Engineering

December 2023

68 pages

ISBN:9798400703751

DOI:10.1145/3617555

General Chairs:
Shane McIntosh
University of Waterloo, Canada
,
Eunjong Choi
Kyoto Institute of Technology, Japan
,
Steffen Herbold
University of Passau, Germany

Copyright © 2023 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 the author(s) 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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SIGSOFT: ACM Special Interest Group on Software Engineering

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 December 2023

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Funding Sources

CHAIR (Chalmers AI Research Center) project ?T4AI?, the National Science Centre of Poland, Vinnova, Software Center, Volvo Cars, AB Volvo, and the National Science Centre of Poland

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PROMISE '23

Sponsor:

SIGSOFT

PROMISE '23: 19th International Conference on Predictive Models and Data Analytics in Software Engineering

December 8, 2023

CA, San Francisco, USA

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Overall Acceptance Rate 98 of 213 submissions, 46%

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Cited By

Azeez HSharbaf MZamani BKolahdouz-Rahimi S(2024)Applying Pattern Language to Enhance IIoT System Design and Integration: From Theory to PracticeInformation10.3390/info1510059515:10(595)Online publication date: 30-Sep-2024
https://doi.org/10.3390/info15100595
Dey TLoungani JIvers JShang WLamothe MWan Z(2024)Smarter Project Selection for Software Engineering ResearchProceedings of the 20th International Conference on Predictive Models and Data Analytics in Software Engineering10.1145/3663533.3664037(12-21)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3663533.3664037

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