extended-abstract

Detecting architecturally-relevant code smells in evolving software systems

Author:

Isela Macia BertranAuthors Info & Claims

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

Pages 1090 - 1093

https://doi.org/10.1145/1985793.1986003

Published: 21 May 2011 Publication History

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Abstract

Refactoring tends to avoid the early deviation of a program from its intended architecture design. However, there is little knowledge about whether the manifestation of code smells in evolving software is indicator of architectural deviations. A fundamental difficulty in this process is that developers are only equipped with static analysis techniques for the source code, which do not exploit traceable architectural information. This work addresses this problem by: (1) identifying a family of architecturally-relevant code smells; (2) providing empirical evidence about the correlation of code smell patterns and architectural degeneration; (3) proposing a set of metrics and detection strategies and that exploit traceable architectural information in smell detection; and (4) conceiving a technique to support the early identification of architecture degeneration symptoms by reasoning about code smell patterns.

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Cited By

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Mohammed MAlshayeb MHassine J(2024)A rule-based approach for the identification of quality improvement opportunities in GRL modelsSoftware Quality Journal10.1007/s11219-024-09679-z32:3(1007-1037)Online publication date: 6-Jun-2024
https://doi.org/10.1007/s11219-024-09679-z
Wan ZZhang YXia XJiang YLo DChandra SBlincoe KTonella P(2023)Software Architecture in Practice: Challenges and OpportunitiesProceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3611643.3616367(1457-1469)Online publication date: 30-Nov-2023
https://dl.acm.org/doi/10.1145/3611643.3616367
Jin WZhong DCai YKazman RLiu T(2023)Evaluating the Impact of Possible Dependencies on Architecture-Level MaintainabilityIEEE Transactions on Software Engineering10.1109/TSE.2022.317128849:3(1064-1085)Online publication date: 1-Mar-2023
https://doi.org/10.1109/TSE.2022.3171288
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Index Terms

Detecting architecturally-relevant code smells in evolving software systems
1. General and reference
  1. Cross-computing tools and techniques
    1. Metrics
2. Social and professional topics
  1. Professional topics
    1. Management of computing and information systems
      1. Software management
        Software maintenance

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Information

Published In

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

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 May 2011

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ICSE11

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SIGSOFT

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

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Mohammed MAlshayeb MHassine J(2024)A rule-based approach for the identification of quality improvement opportunities in GRL modelsSoftware Quality Journal10.1007/s11219-024-09679-z32:3(1007-1037)Online publication date: 6-Jun-2024
https://doi.org/10.1007/s11219-024-09679-z
Wan ZZhang YXia XJiang YLo DChandra SBlincoe KTonella P(2023)Software Architecture in Practice: Challenges and OpportunitiesProceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3611643.3616367(1457-1469)Online publication date: 30-Nov-2023
https://dl.acm.org/doi/10.1145/3611643.3616367
Jin WZhong DCai YKazman RLiu T(2023)Evaluating the Impact of Possible Dependencies on Architecture-Level MaintainabilityIEEE Transactions on Software Engineering10.1109/TSE.2022.317128849:3(1064-1085)Online publication date: 1-Mar-2023
https://doi.org/10.1109/TSE.2022.3171288
Tamburri DKazman RFahimi H(2023)On the Relationship Between Organizational Structure Patterns and Architecture in Agile TeamsIEEE Transactions on Software Engineering10.1109/TSE.2022.315041549:1(325-347)Online publication date: 1-Jan-2023
https://doi.org/10.1109/TSE.2022.3150415
Nanadani HSaad MSharma T(2023)Calibrating Deep Learning-based Code Smell Detection using Human Feedback2023 IEEE 23rd International Working Conference on Source Code Analysis and Manipulation (SCAM)10.1109/SCAM59687.2023.00015(37-48)Online publication date: 2-Oct-2023
https://doi.org/10.1109/SCAM59687.2023.00015
Rachow PWiese Mvan Hoorn A(2023)Taxonomy of Architecture Maintainability Smells2023 30th Asia-Pacific Software Engineering Conference (APSEC)10.1109/APSEC60848.2023.00013(31-40)Online publication date: 4-Dec-2023
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Rachow PRiebisch MErnst NLenarduzzi VSharma T(2022)An architecture smell knowledge base for managing architecture technical debtProceedings of the International Conference on Technical Debt10.1145/3524843.3528092(1-10)Online publication date: 16-May-2022
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Oliveira DAssunção WGarcia AFonseca BRibeiro M(2022)Developers’ perception matters: machine learning to detect developer-sensitive smellsEmpirical Software Engineering10.1007/s10664-022-10234-227:7Online publication date: 12-Oct-2022
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