Published May 5, 2021 | Version v1
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A Systematic Review of Code Smell Detection Approaches

Authors/Creators

  • 1. Research Assistant, Department of Information Technology, Faculty of Information Technology, University of Moratuwa, Sri Lanka.

Description

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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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., Guéhéneuc, 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 ađuzović-Ha žia ić, K., & Spahić, 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).