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Prioritizing God Class Code Smells in Object-Oriented Software Using Fuzzy Inference System

  • Research Article-Computer Engineering and Computer Science
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Abstract

Code Smell is a term that indicates flaws in design and coding practice. God Class is a type of code smell that shows an irregular distribution of functionalities in large-sized classes. God Classes are less cohesive and more coupled in nature, thereby increasing software maintenance efforts and costs. Refactoring all such classes can disturb other related classes with code smell instances, puzzle the developers, and increase the refactoring budget. This paper proposes an automated method to prioritize God Class smell-associated classes with the fuzzy inference system. The fuzzy inference system is used to fuzzy the selected criteria—number of code smell instances, type of code smells, and changes in history. For effective refactoring, first, we moderate the dataset with the CodeMR tool and then highlight that the prioritization criteria are imperative after detecting code smells. Using five metric-based heuristics, a comparative result analysis is done to determine the fore reason for correlation (40–43%) with our results and the gravity of our prioritization criteria. Finally, we provide a severity index of classes with five type classifications and evaluate runtime performance (in seconds) to improve quality.

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Data availability

Available on request.

Notes

  1. https://www.codemr.co.uk/.

  2. https://sites.google.com/site/santiagoavidal/projects/jspirit.

  3. https://github.com/wumpz/jhotdraw.

References

  1. Fowler, M.; Beck, K.; Brant, J.; Opdyke, W.: Refactoring: Improving the Design of Existing Code (2002)

  2. Doğan, E.; Tüzün, E.: Towards a taxonomy of code review smells. Inf. Softw. Technol.Softw. Technol. (2022). https://doi.org/10.1016/j.infsof.2021.106737

    Article  Google Scholar 

  3. Shahidi, M.; Ashtiani, M.; Zakeri-Nasrabadi, M.: An automated extract method refactoring approach to correct the long method code smell. J. Syst. Softw.Softw. 187, 111221 (2022). https://doi.org/10.1016/j.jss.2022.111221

    Article  Google Scholar 

  4. Morales, R.; Soh, Z.; Khomh, F.; Antoniol, G.; Chicano, F.: On the use of developers’ context for automatic refactoring of software anti-patterns. J. Syst. Softw.Softw. 128, 236–251 (2017). https://doi.org/10.1016/j.jss.2016.05.042

    Article  Google Scholar 

  5. Palomba, F.; Bavota, G.; Di Penta, M.; Fasano, F.; Oliveto, R.; De Lucia, A.: A large-scale empirical study on the lifecycle of code smell co-occurrences. Inf. Softw. Technol.Softw. Technol. 99, 1–10 (2018). https://doi.org/10.1016/j.infsof.2018.02.004

    Article  Google Scholar 

  6. Sharma, T.; Efstathiou, V.; Louridas, P.; Spinellis, D.: Code smell detection by deep direct-learning and transfer-learning. J. Syst. Softw.Softw. (2021). https://doi.org/10.1016/j.jss.2021.110936

    Article  Google Scholar 

  7. Olbrich, S.M.; Cruzes, D.S.; Sjoberg, D.I.K.: Are all code smells harmful? A study of God Classes and Brain Classes in the evolution of three open source systems. In: 2010 IEEE International Conference on Software Maintenance. pp. 1–10. IEEE (2010)

  8. Vidal, S.A.; Marcos, C.; Díaz-Pace, J.A.: An approach to prioritize code smells for refactoring. Autom. Softw. Eng.. Softw. Eng. 23, 501–532 (2016). https://doi.org/10.1007/s10515-014-0175-x

    Article  Google Scholar 

  9. Alkharabsheh, K.; Alawadi, S.; Kebande, V.R.; Crespo, Y.; Fernández-Delgado, M.; Taboada, J.A.: A comparison of machine learning algorithms on design smell detection using balanced and imbalanced dataset: a study of God class. Inf. Softw. Technol.Softw. Technol. (2022). https://doi.org/10.1016/j.infsof.2021.106736

    Article  Google Scholar 

  10. Alkharabsheh, K.; Alawadi, S.; Ignaim, K.; Zanoon, N.; Crespo, Y.; Manso, E.; Taboada, J.A.: Prioritization of god class design smell: A multi-criteria based approach. J. King Saud Univ. Comput. Inf. Sci. (2022). https://doi.org/10.1016/j.jksuci.2022.09.011

    Article  Google Scholar 

  11. Anquetil, N.; Etien, A.; Andreo, G.; Ducasse, S.: Decomposing God Classes at Siemens. In: Proceedings - 2019 IEEE International Conference on Software Maintenance and Evolution, ICSME 2019. pp. 169–180. Institute of Electrical and Electronics Engineers Inc. (2019)

  12. Palomba, F.; Bavota, G.; Penta, M.D.; Fasano, F.; Oliveto, R.; Lucia, A.D.: On the diffuseness and the impact on maintainability of code smells: a large scale empirical investigation. Empir. Softw. Eng.. Softw. Eng. 23, 1188–1221 (2018). https://doi.org/10.1007/s10664-017-9535-z

    Article  Google Scholar 

  13. Moha, N.; Guéhéneuc, Y.G.; Duchien, L.; Le Meur, A.F.: DECOR: A method for the specification and detection of code and design smells. IEEE Trans. Softw. Eng.Softw. Eng. 36, 20–36 (2010). https://doi.org/10.1109/TSE.2009.50

    Article  Google Scholar 

  14. Kaur, A.; Dhiman, G.: A Review on Search-Based Tools and Techniques to Identify Bad Code Smells in Object-Oriented Systems. In: Advances in Intelligent Systems and Computing. pp. 909–921. Springer Singapore (2019)

  15. Malhotra, R.; Chug, A.; Khosla, P.: Prioritization of Classes for Refactoring. In: Proceedings of the Third International Symposium on Women in Computing and Informatics - WCI ’15. pp. 228–234. ACM Press, New York, New York, USA (2015)

  16. Ge, X.; DuBose, Q.L.; Murphy-Hill, E.: Reconciling manual and automatic refactoring. In: Proceedings - International Conference on Software Engineering. pp. 211–221 (2012)

  17. Lacerda, G.; Petrillo, F.; Pimenta, M.; Guéhéneuc, Y.G.: Code smells and refactoring: a tertiary systematic review of challenges and observations. J. Syst. Softw.Softw. (2020). https://doi.org/10.1016/j.jss.2020.110610

    Article  Google Scholar 

  18. Mens, T.; Tourwé, T.: A survey of software refactoring. IEEE Trans. Softw. Eng.Softw. Eng. 30, 126–139 (2004). https://doi.org/10.1109/TSE.2004.1265817

    Article  Google Scholar 

  19. Verma, R.; Kumar, K.; Verma, H.K.: A Study of Relevant Parameters Influencing Code Smell Prioritization in Object-Oriented Software Systems. Proceedings of IEEE International Conference on Signal Processing,Computing and Control. 2021-Octob, 150–154 (2021). https://doi.org/10.1109/ISPCC53510.2021.9609478

  20. Verma, R.; Kumar, K.; Verma, H.K.: Code smell prioritization in object-oriented software systems: a systematic literature review. J. Softw. Evol. Process. (2023). https://doi.org/10.1002/smr.2536

    Article  Google Scholar 

  21. Vaucher, S.; Khomh, F.; Moha, N.; Guéhéneuc, Y.G.: Tracking design smells: Lessons from a study of God classes. In: Proceedings - Working Conference on Reverse Engineering, WCRE. pp. 145–154 (2009)

  22. Kovačević, A.; Slivka, J.; Vidaković, D.; Grujić, K.G.; Luburić, N.; Prokić, S.; Sladić, G.: Automatic detection of Long Method and God Class code smells through neural source code embeddings. Expert Syst. Appl. (2022). https://doi.org/10.1016/j.eswa.2022.117607

    Article  Google Scholar 

  23. Fontana, F.A.; Ferme, V.; Marino, A.: Is it a Real Code Smell to be Removed or not? (2013)

  24. Gupta, A.; Chauhan, N.K.: A severity-based classification assessment of code smells in Kotlin and java application. Arab. J. Sci. Eng. (2021). https://doi.org/10.1007/s13369-021-06077-6

    Article  PubMed  PubMed Central  Google Scholar 

  25. Paiva, T.; Damasceno, A.; Figueiredo, E.; Sant’Anna, C.: On the evaluation of code smells and detection tools. J. Softw. Eng. Res. Dev. (2017). https://doi.org/10.1186/s40411-017-0041-1

    Article  Google Scholar 

  26. Kaur, S.; Singh, P.: How does object-oriented code refactoring influence software quality? Research landscape and challenges. J. Syst. Softw.Softw. 157, 110394 (2019). https://doi.org/10.1016/j.jss.2019.110394

    Article  Google Scholar 

  27. Lacerda, G.; Petrillo, F.; Pimenta, M.: Code smells and refactoring: a tertiary systematic review of challenges and observations. (2020)

  28. Danphitsanuphan, P.; Suwantada, T.: Code smell detecting tool and code smell-structure bug relationship. In: 2012 Spring World Congress on Engineering and Technology, SCET 2012 - Proceedings (2012)

  29. Yamashita, A.; Moonen, L.: Exploring the impact of inter-smell relations on software maintainability: an empirical study. Proc. Int. Conf. Softw. Eng. (2013). https://doi.org/10.1109/ICSE.2013.6606614

    Article  Google Scholar 

  30. Yamashita, A.; Moonen, L.: To what extent can maintenance problems be predicted by code smell detection? An empirical study. Inf. Softw. Technol.Softw. Technol. 55, 2223–2242 (2013). https://doi.org/10.1016/j.infsof.2013.08.002

    Article  Google Scholar 

  31. Liu, H.; Ma, Z.; Shao, W.; Niu, Z.: Schedule of bad smell detection and resolution: a new way to save effort. IEEE Trans. Software Eng. 38, 220–235 (2012). https://doi.org/10.1109/TSE.2011.9

    Article  Google Scholar 

  32. Fontana, F.A.; Zanoni, M.: On investigating code smells correlations. In: Proceedings - 4th IEEE International Conference on Software Testing, Verification, and Validation Workshops, ICSTW 2011. pp. 474–475 (2011)

  33. Fard, A.M.; Mesbah, A.: JSNOSE: Detecting javascript code smells. In: IEEE 13th International Working Conference on Source Code Analysis and Manipulation, SCAM 2013. pp. 116–125. IEEE Computer Society (2013)

  34. Kiefer, C.; Bernstein, A.; Tappolet, J.: Mining software repositories with iSPARQL and a software evolution ontology. Proceedings - ICSE 2007 Workshops: Fourth International Workshop on Mining Software Repositories, MSR 2007. (2007). https://doi.org/10.1109/MSR.2007.21

  35. Liu, X.; Zhang, C.: The detection of code smell on software development: a mapping study. In: Proceedings of the 2017 5th International Conference on Machinery, Materials and Computing Technology (ICMMCT 2017). pp. 560–575. Atlantis Press, Paris, France (2017)

  36. Fontana, F.A.; Ferme, V.; Marino, A.: Is it a Real Code Smell to be Removed or not? Inter-smell relationships View project PONTRAGA View project Is it a Real Code Smell to be Removed or not?

  37. Fontana, F.A.; Ferme, V.; Zanoni, M.; Roveda, R.: Towards a prioritization of code debt: A code smell Intensity Index. In: 2015 IEEE 7th International Workshop on Managing Technical Debt (MTD). pp. 16–24. IEEE (2015)

  38. Amrita, G.P.; Singh, P.: Priority-wise test case allocation using fuzzy logic. Int. J. Syst. Assurance Eng. Manag. (2021). https://doi.org/10.1007/s13198-021-01247-z

    Article  Google Scholar 

  39. Pecorelli, F.; Palomba, F.; Khomh, F.; De Lucia, A.: Developer-Driven Code Smell Prioritization. In: Proceedings of the 17th International Conference on Mining Software Repositories. pp. 220–231. ACM, New York, NY, USA (2020)

  40. Fontana, F.A.; Mariani, E.; Morniroli, A.; Sormani, R.; Tonello, A.: An experience report on using code smells detection tools. In: Proceedings - 4th IEEE International Conference on Software Testing, Verification, and Validation Workshops, ICSTW 2011. 450–457 (2011). https://doi.org/10.1109/ICSTW.2011.12

  41. Arcoverde, R.; Guimaraes, E.; Macia, I.; Garcia, A.; Cai, Y.: Prioritization of Code Anomalies Based on Architecture Sensitiveness. In: 2013 27th Brazilian Symposium on Software Engineering. pp. 69–78. IEEE (2013)

  42. Oliveira, A.; Sousa, L.; Oizumi, W.; Garcia, A.: On the Prioritization of Design-Relevant Smelly Elements. In: Proceedings of the XIII Brazilian Symposium on Software Components, Architectures, and Reuse - SBCARS ’19. pp. 83–92. ACM Press, New York, New York, USA (2019)

  43. BafandehMayvan, B.; Rasoolzadegan, A.; JavanJafari, A.: Bad smell detection using quality metrics and refactoring opportunities. J. Softw. Evol. Process. (2020). https://doi.org/10.1002/smr.2255

    Article  Google Scholar 

  44. Shahidi, M.; Ashtiani, M.; Zakeri-Nasrabadi, M.: An automated extract method refactoring approach to correct the long method code smell. J. Syst. Softw.Softw. (2022). https://doi.org/10.1016/j.jss.2022.111221

    Article  Google Scholar 

  45. Habchi, S.; Moha, N.; Rouvoy, R.: Android code smells: From introduction to refactoring. J. Syst. Softw.Softw. (2021). https://doi.org/10.1016/j.jss.2021.110964

    Article  Google Scholar 

  46. Danphitsanuphan, P.: Code Smell Detecting Tool and Code Smell-Structure Bug Relationship Code Smell Detecting Tool and Code Smell-Structure Bug Relationship. (2014). https://doi.org/10.1109/SCET.2012.6342082

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Funding

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Dr B R Ambedkar National Institute of Technology, Jalandhar, Punjab, 144011, India

    Renu Verma & Harsh K. Verma

  2. Department of Computer Engineering, National Institute of Technology Kurukshetra, Kurukshetra, Haryana, 136119, India

    Kuldeep Kumar

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  1. Renu Verma
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  3. Harsh K. Verma
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Contributions

Conceptualization: Renu Verma, Kuldeep Kumar; Data Curation: Renu Verma, Kuldeep Kumar; Methodology: Renu Verma, Kuldeep Kumar, Harsh K. Verma, Formal analysis and investigation: Renu Verma, Kuldeep Kumar, Harsh K. Verma; Writing—original draft preparation: Renu Verma, Kuldeep Kumar; Writing—review and editing: Harsh K. Verma.

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Correspondence to Renu Verma.

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Supplementary file1 (JAVA 28 KB)

Appendices

Appendix 1

See Tables

Table 13 GCS1: Ranking list of classes evaluated with metric-based heuristic
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Table 14 GCS2: Ranking list of classes evaluated with metric-based heuristic
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Table 15 GCS3: Ranking list of classes evaluated with metric-based heuristic
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Table 16 GCS4: Ranking list of classes evaluated with metric-based heuristic
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Table 17 GCS5: Ranking list of classes evaluated with metric-based heuristic
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Appendix 2

See Table 

Table 18 Severity index of classes evaluated with metric-based heuristics (GCS1, GCS2, GCS3, GCS4, GCS5)
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18.

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Verma, R., Kumar, K. & Verma, H.K. Prioritizing God Class Code Smells in Object-Oriented Software Using Fuzzy Inference System. Arab J Sci Eng (2024). https://doi.org/10.1007/s13369-024-08826-9

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