Abstract
Many code smell detection techniques and tools have been proposed, mainly aiming to eliminate design flaws and improve software quality. Most of them are based on heuristics which rely on a set of software metrics and corresponding threshold values. Those techniques and tools suffer from subjectivity issues, discordant results among the tools, and the reliability of the thresholds. To mitigate these problems, we used machine learning to automate developers’ perception in code smells detection. Different from other existing machine learning used in code smell detection we trained our models with an extensive dataset based on more than 3000 professional reviews on 518 open source projects. We conclude by an empirical evaluation of the performance of the machine learning approach against PMD, a widely used metric-based code smell detection tool for Java. The experimental results show that the machine learning approach outperforms the PMD classifier in all evaluations.
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References
Amorim, L., Costa, E., Antunes, N., et al.: Experience report: evaluating the effectiveness of decision trees for detecting code smells. In: 2015 IEEE 26th International Symposium on ISSRE, pp. 261–269. IEEE (2015)
Azeem, M.I., Palomba, F., Shi, L., et al.: Machine learning techniques for code smell detection: a systematic literature review and meta-analysis. Inf. Softw. Technol. 108, 115–138 (2019)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Chidamber, S.R., Kemerer, C.F.: Towards a metrics suite for object oriented design. In: Conference Proceedings on Object-Oriented Programming Systems, Languages, and Applications, pp. 197–211 (1991)
Di Nucci, D., Palomba, F., Tamburri, D.A., et al.: Detecting code smells using machine learning techniques: are we there yet? In: 2018 IEEE 25th International Conference on SANER, pp. 612–621. IEEE (2018)
Elkhail, A.A., Cerny, T.: On relating code smells to security vulnerabilities. In: 2019 IEEE 5th International Conference on BigDataSecurity, IEEE International Conference on HPSC, and IEEE International Conference on IDS, pp. 7–12. IEEE (2019)
Fernandes, E., Oliveira, J., Vale, G., et al.: A review-based comparative study of bad smell detection tools. In: Proceedings of the 20th International Conference on Evaluation and Assessment in Software Engineering, pp. 1–12 (2016)
Arcelli Fontana, F., Mäntylä, M.V., Zanoni, M., et al.: Comparing and experimenting machine learning techniques for code smell detection. Empir. Softw. Eng. 21, 1143–1191 (2016). https://doi.org/10.1007/s10664-015-9378-4
Fontana, F.A., Zanoni, M.: Code smell severity classification using machine learning techniques. Knowl. Based Syst. 128, 43–58 (2017)
Fowler, M., Beck, K., Brant, W., et al.: Refactoring: Improving the Design of Existing Code. Addison-Wesley Longman Publishing Co. Inc., Boston, USA (1999)
Fowler, M.: Refactoring: Improving the Design of Existing Code. Addison-Wesley Professional (2018)
Kamaraj, N., Ramani, A.: Search-based software engineering approach for detecting code-smells with development of unified model for test prioritization strategies. Int. J. Appl. Eng. Res. 14(7), 1599–1603 (2019)
Kaur, A., Jain, S., Goel, S., et al.: A review on machine-learning based code smell detection techniques in object-oriented software system(s). Recent Adv. Electr. Electron. Eng. 2021(14), 290–303 (2021)
Kreimer, J.: Adaptive detection of design flaws. Electron. Notes Theoret. Comput. Sci. 141(4), 117–136 (2005)
Lanza, M., Marinescu, R.: Object-Oriented Metrics in Practice: Using Software Metrics to Characterize, Evaluate, and Improve the Design of Object-Oriented Systems. Springer, Heidelberg (2006). https://doi.org/10.1007/3-540-39538-5
Liu, H., Jin, J., Xu, Z., et al.: Deep learning based code smell detection. IEEE Trans. Softw. Eng. 47, 1811–1837 (2019)
Madeyski, L., Lewowski, T.: MLCQ: industry-relevant code smell data set. In: Proceedings of the Evaluation and Assessment in Software Engineering, pp. 342–347 (2020)
Martin, R.C.: Clean Code: A Handbook of Agile Software Craftsmanship, 1st edn. Prentice Hall, USA (2008)
Paiva, T., Damasceno, A., Figueiredo, E., Sant’Anna, C.: On the evaluation of code smells and detection tools. J. Softw. Eng. Res. Develop. 5(1), 1–28 (2017). https://doi.org/10.1186/s40411-017-0041-1
Pecorelli, F., Palomba, F., Khomh, F., et al.: Developer-driven code smell prioritization. In: Proceedings of the the 17th International Conference on MSR, pp. 220–231 (2020)
Pedregosa, F., Varoquaux, G., Gramfort, A., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
PMD: an extensible cross-language static code analyzer. https://pmd.github.io. Accessed 31 May 2021
Quinlan, J.R.: Learning decision tree classifiers. ACM Comput. Surv. (CSUR) 28(1), 71–72 (1996)
Rasool, G., Arshad, Z.: A review of code smell mining techniques. J. Softw. Evol. Process 27(11), 867–895 (2015)
Santos, J.A.M., Rocha-Junior, J.B., Prates, L.C.L., et al.: A systematic review on the code smell effect. J. Syst. Softw. 144, 450–477 (2018)
Understand by SciTools. https://www.scitools.com/. Accessed 31 May 2021
Sharma, T., Efstathiou, V., Louridas, P., et al.: Code smell detection by deep direct-learning and transfer-learning. J. Syst. Softw. 176, 110936 (2021)
Sirikul, K., Soomlek, C.: Automated detection of code smells caused by null checking conditions in Java programs. In: 2016 13th International Joint Conference on Computer Science and Software Engineering (JCSSE), pp. 1–7. IEEE (2016)
Vanschoren, J., Van Rijn, J.N., Bischl, B., Torgo, L.: OpenML: networked science in machine learning. ACM SIGKDD Expl. Newsl. 15(2), 49–60 (2014)
Yamashita, A., Moonen, L.: To what extent can maintenance problems be predicted by code smell detection? An empirical study. Inf. Softw. Technol. 55(12), 2223–2242 (2013)
Acknowledgements
We would like to thank Khon Kaen University for awarding KKU Outbound Visiting Scholarship to the first author.
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Soomlek, C., van Rijn, J.N., Bonsangue, M.M. (2021). Automatic Human-Like Detection of Code Smells. In: Soares, C., Torgo, L. (eds) Discovery Science. DS 2021. Lecture Notes in Computer Science(), vol 12986. Springer, Cham. https://doi.org/10.1007/978-3-030-88942-5_2
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DOI: https://doi.org/10.1007/978-3-030-88942-5_2
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