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A Systematic Review of Code Smell Detection Approaches

Authors: Chathuranga Hasantha;

A Systematic Review of Code Smell Detection Approaches

Abstract

{"references": ["Fowler, M. (2018). Refactoring: improving the design of existing code. Addison-Wesley Professional.", "Maduranga, M. A. K., Mahagamage, D. C., Madhavi, P. I., Madushan, J. A. H., & Wijesiriwardana, C. (2016). Domain specific infrastructure for code smell detection in large-scale software systems. In Sri Lanka: International Research Symposium on Engineering Advancements.", "Abeyrathna, A., Samarage, C., Dahanayake, B., Wijesiriwardana, C., & Wimalaratne, P. (2020). A security specific knowledge modelling approach for secure software engineering. Journal of the National Science Foundation of Sri Lanka, 48(1).", "Moha, N., Gu\u00e9h\u00e9neuc, Y. G., Duchien, L., & Le Meur, A. F. (2009). Decor: A method for the specification and detection of code and design smells. IEEE Transactions on Software Engineering, 36(1), 20-36.", "Pessoa, T., Monteiro, M. P., & Bryton, S. (2012). An eclipse plugin to support code smells detection. arXiv preprint arXiv:1204.6492.", "Ka a\u0111uzovi\u0107-Ha \u017eia i\u0107, K., & Spahi\u0107, R. (2018, Septem e ). Comparison of machine learning methods for code smell detection using reduced features. In 2018 3rd International Conference on Computer Science and Engineering (UBMK) (pp. 670-672). IEEE.", "Shahin, M., Liang, P., & Babar, M. A. (2014). A systematic review of software architecture visualization techniques. Journal of Systems and Software, 94, 161-185.", "Pecorelli, F., Palomba, F., Di Nucci, D., & De Lucia, A. (2019, May). Comparing heuristic and machine learning approaches for metric-based code smell detection. In 2019 IEEE/ACM 27th International Conference on Program Comprehension (ICPC) (pp. 93-104). IEEE.", "Lanza, M., & Marinescu, R. (2007). Object-oriented metrics in practice: using software metrics to characterize, evaluate, and improve the design of object-oriented systems. Springer Science & Business Media.", "Schumacher, J., Zazworka, N., Shull, F., Seaman, C., & Shaw, M. (2010, September). Building empirical support for automated code smell detection. In Proceedings of the 2010 ACM-IEEE international symposium on empirical software engineering and measurement (pp. 1-10)."]}

Code smells are symptoms of design shortcomings in source code. There are various tools and approaches have been proposed for detecting code smells. A systematic review (PRISMA) has been performed based on the search of digital libraries that includes the publications in the last decade. 70 research papers are analyzed and provide an extensive overview of existing code smell detection approaches, current trends in code smells detection, potential areas of code smell detection using new technologies. These results will facilitate developers to understand their real needs when further research on code smell detection.

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Keywords

code smell, code smell detection, detection approaches, current trends

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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