Article

Micro pattern evolution

Authors:

Sunghun Kim,

Kai Pan,

E. James Whitehead, Jr.Authors Info & Claims

MSR '06: Proceedings of the 2006 international workshop on Mining software repositories

Pages 40 - 46

https://doi.org/10.1145/1137983.1137995

Published: 22 May 2006 Publication History

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Abstract

When analyzing the evolution history of a software project, we wish to develop results that generalize across projects. One approach is to analyze design patterns, permitting characteristics of the evolution to be associated with patterns, instead of source code. Traditional design patterns are generally not amenable to reliable automatic extraction from source code, yet automation is crucial for scalable evolution analysis. Instead, we analyze "micro pattern" evolution; patterns whose abstraction level is closer to source code, and designed to be automatically extractable from Java source code or bytecode. We perform micro-pattern evolution analysis on three open source projects, ArgoUML, Columba, and jEdit to identify micro pattern frequencies, common kinds of pattern evolution, and bug-prone patterns. In all analyzed projects, we found that the micro patterns of Java classes do not change often. Common bug-prone pattern evolution kinds are 'Pool → Pool', 'Implementor → NONE', and 'Sampler → Sampler'. Among all pattern evolution kinds, 'Box', 'CompoundBox', 'Pool', 'CommonState', and 'Outline' micro patterns have high bug rates, but they have low frequencies and a small number of changes. The pattern evolution kinds that are bug-prone are somewhat similar across projects. The bug-prone pattern evolution kinds of two different periods of the same project are almost identical.

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Cited By

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Lu TWang XJia T(2024)Design Pattern Representation and Detection Based on Heterogeneous Information NetworkReuse and Software Quality10.1007/978-3-031-66459-5_5(72-91)Online publication date: 13-Jul-2024
https://doi.org/10.1007/978-3-031-66459-5_5
Trivedi AThakur JGupta AJain SGupta ALo DSaha DSharma R(2020)Code Nano-Pattern Detection using Deep LearningProceedings of the 13th Innovations in Software Engineering Conference (formerly known as India Software Engineering Conference)10.1145/3385032.3385050(1-6)Online publication date: 27-Feb-2020
https://dl.acm.org/doi/10.1145/3385032.3385050
Panichella SZaugg N(2020)An Empirical Investigation of Relevant Changes and Automation Needs in Modern Code ReviewEmpirical Software Engineering10.1007/s10664-020-09870-3Online publication date: 13-Sep-2020
https://doi.org/10.1007/s10664-020-09870-3
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Index Terms

Micro pattern evolution
1. Social and professional topics
  1. Professional topics
    1. Management of computing and information systems
      1. Software management
        Software maintenance
2. Software and its engineering
  1. Software creation and management
    1. Software development techniques
      1. Reusability
        Software product lines
    2. Software post-development issues
      1. Software reverse engineering

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Published In

MSR '06: Proceedings of the 2006 international workshop on Mining software repositories

May 2006

191 pages

ISBN:1595933972

DOI:10.1145/1137983

General Chairs:
Stephan Diehl
University Trier, Germany
,
Harald Gall
University of Zurich, Switzerland
,
Ahmed E. Hassan
Research in Motion RIM, Canada

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 May 2006

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ICSE06: International Conference on Software Engineering

May 22 - 23, 2006

Shanghai, China

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Cited By

View all

Lu TWang XJia T(2024)Design Pattern Representation and Detection Based on Heterogeneous Information NetworkReuse and Software Quality10.1007/978-3-031-66459-5_5(72-91)Online publication date: 13-Jul-2024
https://doi.org/10.1007/978-3-031-66459-5_5
Trivedi AThakur JGupta AJain SGupta ALo DSaha DSharma R(2020)Code Nano-Pattern Detection using Deep LearningProceedings of the 13th Innovations in Software Engineering Conference (formerly known as India Software Engineering Conference)10.1145/3385032.3385050(1-6)Online publication date: 27-Feb-2020
https://dl.acm.org/doi/10.1145/3385032.3385050
Panichella SZaugg N(2020)An Empirical Investigation of Relevant Changes and Automation Needs in Modern Code ReviewEmpirical Software Engineering10.1007/s10664-020-09870-3Online publication date: 13-Sep-2020
https://doi.org/10.1007/s10664-020-09870-3
Zhang TYang DLopes CKim MAtlee JBultan TWhittle J(2019)Analyzing and supporting adaptation of online code examplesProceedings of the 41st International Conference on Software Engineering10.1109/ICSE.2019.00046(316-327)Online publication date: 25-May-2019
https://dl.acm.org/doi/10.1109/ICSE.2019.00046
Sultana KWilliams BBhowmik T(2019)A study examining relationships between micro patterns and security vulnerabilitiesSoftware Quality Journal10.1007/s11219-017-9397-z27:1(5-41)Online publication date: 1-Mar-2019
https://dl.acm.org/doi/10.1007/s11219-017-9397-z
Sultana KWilliams BBosu A(2018)A Comparison of Nano-Patterns vs. Software Metrics in Vulnerability Prediction2018 25th Asia-Pacific Software Engineering Conference (APSEC)10.1109/APSEC.2018.00050(355-364)Online publication date: Dec-2018
https://doi.org/10.1109/APSEC.2018.00050
Wu RWen MCheung SZhang H(2018)ChangeLocatorEmpirical Software Engineering10.1007/s10664-017-9567-423:5(2866-2900)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.1007/s10664-017-9567-4
Sultana KRosu GDi Penta MNguyen T(2017)Towards a software vulnerability prediction model using traceable code patterns and software metricsProceedings of the 32nd IEEE/ACM International Conference on Automated Software Engineering10.5555/3155562.3155700(1022-1025)Online publication date: 30-Oct-2017
https://dl.acm.org/doi/10.5555/3155562.3155700
Sultana KWilliams B(2017)Evaluating micro patterns and software metrics in vulnerability prediction2017 6th International Workshop on Software Mining (SoftwareMining)10.1109/SOFTWAREMINING.2017.8100852(40-47)Online publication date: Nov-2017
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Sultana K(2017)Towards a software vulnerability prediction model using traceable code patterns and software metrics2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)10.1109/ASE.2017.8115724(1022-1025)Online publication date: Oct-2017
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