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Effectiveness of encapsulation and object-oriented metrics to refactor code and identify error prone classes using bad smells

Authors:

Satwinder Singh,

K. S. KahlonAuthors Info & Claims

ACM SIGSOFT Software Engineering Notes, Volume 36, Issue 5

Pages 1 - 10

https://doi.org/10.1145/2020976.2020994

Published: 30 September 2011 Publication History

Abstract

To assist maintenance and evolution teams, work needs to be done at the onset of software development. One such facilitation is refactoring the code, making it easier to read, understand and maintain. Refactoring is done by identifying bad smell areas in the code. In this paper, based on empirical analysis, we develop a metrics model to identify smelly classes. The role of two new metrics (encapsulation and information hiding) is also investigated for identifying smelly and faulty classes in software code. This paper first presents a binary statistical analysis of thev relationship between metrics and bad smells, the results of which show a significant relationship. Then, the metrics model (with significant metrics shortlisted from the binary analysis) for bad smell categorization (divided into five categories) is developed. To verify our model, we examine the open source Firefox system, which has a strong industrial usage. The results show that proposed metrics model for bad smell can predict faulty classes with high accuracy, but in the case of the categorized model not all categories of bad smells can adequately identified the faulty and smelly classes. Due to certain limitations of our study more experiments are required to generalize the results of bad smell and faulty class identification in software code.

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Mohammed MAlshayeb MHassine J(2022)A search-based approach for detecting circular dependency bad smell in goal-oriented modelsSoftware and Systems Modeling (SoSyM)10.1007/s10270-021-00965-z21:5(2007-2037)Online publication date: 1-Oct-2022
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Index Terms

Effectiveness of encapsulation and object-oriented metrics to refactor code and identify error prone classes using bad smells
1. General and reference
  1. Cross-computing tools and techniques
    1. Metrics

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Information

Published In

cover image ACM SIGSOFT Software Engineering Notes

ACM SIGSOFT Software Engineering Notes Volume 36, Issue 5

September 2011

160 pages

ISSN:0163-5948

DOI:10.1145/2020976

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Copyright © 2011 Authors.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 September 2011

Published in SIGSOFT Volume 36, Issue 5

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Cited By

Mindom PNikanjam AKhomh F(2024)Harnessing pre-trained generalist agents for software engineering tasksEmpirical Software Engineering10.1007/s10664-024-10597-830:1Online publication date: 11-Dec-2024
https://dl.acm.org/doi/10.1007/s10664-024-10597-8
Kaur SSingh S(2023)Improving the Quality of Open Source SoftwareAgile Software Development10.1002/9781119896838.ch16(309-323)Online publication date: 8-Feb-2023
https://doi.org/10.1002/9781119896838.ch16
Mohammed MAlshayeb MHassine J(2022)A search-based approach for detecting circular dependency bad smell in goal-oriented modelsSoftware and Systems Modeling (SoSyM)10.1007/s10270-021-00965-z21:5(2007-2037)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1007/s10270-021-00965-z
Mohammed MHassine JAlshayeb M(2022)GSDetector: a tool for automatic detection of bad smells in GRL goal modelsInternational Journal on Software Tools for Technology Transfer (STTT)10.1007/s10009-022-00662-224:6(889-910)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1007/s10009-022-00662-2
Korlepara PGrigoriou MKontogiannis KBrealey CGiammaria AOnuţ IZulkernine F(2021)Combining domain expert knowledge and machine learning for the identification of error prone filesProceedings of the 31st Annual International Conference on Computer Science and Software Engineering10.5555/3507788.3507810(153-162)Online publication date: 22-Nov-2021
https://dl.acm.org/doi/10.5555/3507788.3507810
Lee SWong WLi YChu W(2021)Software Fault-Proneness Analysis based on Composite Developer-Module NetworksIEEE Access10.1109/ACCESS.2021.31284389(155314-155334)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3128438
AbuHassan AAlshayeb MGhouti L(2021)Software smell detection techniquesJournal of Software: Evolution and Process10.1002/smr.232033:3Online publication date: 3-Mar-2021
https://dl.acm.org/doi/10.1002/smr.2320
Ghareb MAllen G(2018)State of the art metrics for aspect oriented programming10.1063/1.5032069(020107)Online publication date: 2018
https://doi.org/10.1063/1.5032069
Singh SKaur S(2018)A systematic literature review: Refactoring for disclosing code smells in object oriented softwareAin Shams Engineering Journal10.1016/j.asej.2017.03.0029:4(2129-2151)Online publication date: Dec-2018
https://doi.org/10.1016/j.asej.2017.03.002
Kaur KKaur P(2017)Evaluation of sampling techniques in software fault prediction using metrics and code smells2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI)10.1109/ICACCI.2017.8126033(1377-1387)Online publication date: Sep-2017
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