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10.1109/QUATIC.2010.16guideproceedingsArticle/Chapter ViewAbstractPublication PagesConference Proceedingsacm-pubtype
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Investigating the Evolution of Bad Smells in Object-Oriented Code

Published: 29 September 2010 Publication History

Abstract

Software design problems are known and perceived under many different terms such as bad smells, flaws, non-compliance to design principles, violation of heuristics, excessive metric values and antipatterns, signifying the importance of handling them in the construction and maintenance of software. Once a design problem is identified, it can be removed by applying an appropriate refactoring, improving in most cases several aspects of quality such as maintainability, comprehensibility and reusability. This paper, taking advantage of recent advances and tools in the identification of non-trivial bad smells, explores the presence and evolution of such problems by analyzing past versions of code. Several interesting questions can be investigated such as whether the number of problems increases with the passage of software generations, whether problems vanish by time or only by targeted human intervention, whether bad smells occur in the course of evolution of a module or exist right from the beginning and whether refactorings targeting at smell removal are frequent. In contrast to previous studies that investigate the application of refactorings in the history of a software project, we attempt to study the subject from the point of view of the problems themselves distinguishing deliberate maintenance activities from the removal of design problems as a side effect of software evolution. Results are discussed for two open-source systems and three bad smells.

Cited By

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  • (2023)Fusion of deep convolutional and LSTM recurrent neural networks for automated detection of code smellsProceedings of the 27th International Conference on Evaluation and Assessment in Software Engineering10.1145/3593434.3593476(229-234)Online publication date: 14-Jun-2023
  • (2023)The Smelly Eight: An Empirical Study on the Prevalence of Code Smells in Quantum ComputingProceedings of the 45th International Conference on Software Engineering10.1109/ICSE48619.2023.00041(358-370)Online publication date: 14-May-2023
  • (2023)PHP code smells in web appsJournal of Systems and Software10.1016/j.jss.2023.111644200:COnline publication date: 1-Jun-2023
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Published In

cover image Guide Proceedings
QUATIC '10: Proceedings of the 2010 Seventh International Conference on the Quality of Information and Communications Technology
September 2010
513 pages
ISBN:9780769542416

Publisher

IEEE Computer Society

United States

Publication History

Published: 29 September 2010

Author Tags

  1. bad smell
  2. evolution
  3. refactoring
  4. software history
  5. software repositories

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Cited By

View all
  • (2023)Fusion of deep convolutional and LSTM recurrent neural networks for automated detection of code smellsProceedings of the 27th International Conference on Evaluation and Assessment in Software Engineering10.1145/3593434.3593476(229-234)Online publication date: 14-Jun-2023
  • (2023)The Smelly Eight: An Empirical Study on the Prevalence of Code Smells in Quantum ComputingProceedings of the 45th International Conference on Software Engineering10.1109/ICSE48619.2023.00041(358-370)Online publication date: 14-May-2023
  • (2023)PHP code smells in web appsJournal of Systems and Software10.1016/j.jss.2023.111644200:COnline publication date: 1-Jun-2023
  • (2023)The lifecycle of Technical Debt that manifests in both source code and issue trackersInformation and Software Technology10.1016/j.infsof.2023.107216159:COnline publication date: 1-Jul-2023
  • (2022)Does it matter who pays back Technical Debt? An empirical study of self-fixed TDInformation and Software Technology10.1016/j.infsof.2021.106738143:COnline publication date: 1-Mar-2022
  • (2020)An empirical study on self-fixed technical debtProceedings of the 3rd International Conference on Technical Debt10.1145/3387906.3388621(11-20)Online publication date: 28-Jun-2020
  • (2020)The Scent of Deep Learning CodeProceedings of the 17th International Conference on Mining Software Repositories10.1145/3379597.3387479(420-430)Online publication date: 29-Jun-2020
  • (2020)On the diffuseness of technical debt items and accuracy of remediation time when using SonarQubeInformation and Software Technology10.1016/j.infsof.2020.106377128:COnline publication date: 1-Dec-2020
  • (2019)On the role of data balancing for machine learning-based code smell detectionProceedings of the 3rd ACM SIGSOFT International Workshop on Machine Learning Techniques for Software Quality Evaluation10.1145/3340482.3342744(19-24)Online publication date: 27-Aug-2019
  • (2019)Comparative Study for Detecting Mobile Application's Anti-PatternsProceedings of the 8th International Conference on Software and Information Engineering10.1145/3328833.3328834(1-8)Online publication date: 9-Apr-2019
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