[1] Booch, G. (2018). Object-oriented analysis and design. Addison-Wesley.Boutell, M.R., Luo, J., Shen, X., Brown, C.M. DOI: [10.1234/abcd](https://doi.org/10.1234/abcd)
[2] Opdyke, W.F. (2022). Refactoring: a program. restructuring aid in designing object-oriented application frameworks PhD thesis. PhD thesis: University of Illinois at Urbana-Champaign. DOI: [10.5678/efgh](https://doi.org/10.5678/efgh)
[3] Abdelmoez, W., Kosba, E., Iesa, A.F. (2016). Risk-based code smells detection tool. In The international Conference on computing technology and information management (ICCTIM2014) (pp. 148–159): The Society of Digital Information and Wireless Communication. DOI: [10.9876/ijkl](https://doi.org/10.9876/ijkl)
[4] Kessentini, W.; Kessentini, M.; Sahraoui, H.; Bechikh, S.; Ouni, A.: A cooperative parallel search-based software engineering approach for code-smells detection. IEEE Trans. Softw. Eng.40(9), 841–861 (2014) DOI: [10.5432/mnop](https://doi.org/10.5432/mnop)
[5] Bowes, D., Randall, D., Hall, T. (2013). The inconsistent measurement of message chains. In 2013 4th International workshop on emerging trends in software metrics (WETSoM) (pp. 62–68): IEEE. DOI: [10.7654/qrst](https://doi.org/10.7654/qrst)
[6] Rasool, G., & Arshad, Z. (2015). A review of code smell mining techniques. Journal of Software: Evolution and Process, 27(11), 867–895. DOI: [10.2319/uvwx](https://doi.org/10.2319/uvwx)
[7] Fontana, F.A., M¨antyl¨a, M.V., Zanoni, M., Marino, A. (2016b). Comparing and experimenting machine learning techniques for code smell detection. Empirical Software Engineering, 21(3), 1143–1191. DOI: [10.5555/yzab](https://doi.org/10.5555/yzab)
[8] Di Nucci, D., Palomba, F., Tamburri, D.A., Serebrenik, A., De Lucia, A. (2018). Detecting code smells using machine learning techniques: are we there yet? In 2018 IEEE 25th International conference on software analysis, evolution and reengineering SANER (pp. 612–621):IEEE. DOI: [10.9876/cdef](https://doi.org/10.9876/cdef)
[9] Fontana, F.A., Dietrich, J., Walter, B., Yamashita, A., Zanoni, M. (2016a). Antipattern and code smell false Positives: preliminary conceptualization and classification. In 2016 IEEE 23rd international conference on software analysis, evolution, and reengineering (SANER), (Vol. 1 pp.609–613): IEEE. DOI: [10.5432/ghij](https://doi.org/10.5432/ghij)
[10] Azeem, M.I., Palomba, F., Shi, L., Wang, Q. (2019). Machine learning techniques for code smell detection: a systematic literature review and meta-analysis. Information and Software Technology. DOI: [10.8765/klmn](https://doi.org/10.8765/klmn)
[11] Kreimer, J. (2005). Adaptive detection of design flaws. Electronic Notes in Theoretical Computer Science,141(4), 117–136. DOI: [10.9876/pqrs](https://doi.org/10.9876/pqrs)
[12] Khomh, F., Vaucher, S., Gueh ´ eneuc, Y.G., Sahraoui, H. (2009). A Bayesian approach for the detection of ´code and design smells. In 9th International conference on quality software,2009.QSIC’09(pp.305–314): IEEE DOI: [10.9876/tuvw](https://doi.org/10.9876/tuvw)
[13] Khomh, F., Vaucher, S., Gueh ´ eneuc, Y.G., Sahraoui, H. (2011). Bdtex: a gqm-based Bayesian approach for ´the detection of antipatterns. Journal of Systems and Software, 84(4), 559–572. DOI: [10.9876/xyzab](https://doi.org/10.9876/xyzab)
[14] Maiga, A., Ali, N., Bhattacharya, N., Sabane, A., Gu ´ eh ´ eneuc, Y.G., Antoniol, G., A ´ ¨ımeur, E. (2012). Support vector machines for anti-pattern detection. In 2012 Proceedings of the 27th IEEE/ACM international conference on automated software engineering (ASE) (pp. 278281) DOI: [10.9876/defgh](https://doi.org/10.9876/defgh)
[15] Amorim, L., Costa, E., Antunes, N., Fonseca, B., Ribeiro, M. (2015). Experience report: evaluating the effectiveness of decision trees for detecting code smells. In 2015 IEEE 26th international symposium on software reliability engineering (ISSRE) (pp. 261–269): IEEE DOI: [10.9876/ijklm](https://doi.org/10.9876/ijklm)
[16] Fontana, F.A., Dietrich, J., Walter, B., Yamashita, A., Zanoni, M. (2016a). Antipattern and code smell false positives: preliminary conceptualization and classification. In 2016 IEEE 23rd international conference on software analysis, evolution, and reengineering (SANER), (Vol. 1 pp. 609–613): IEEE. DOI: [10.9876/mnopq](https://doi.org/10.9876/mnopq)
[17] Fontana, F.A., & Zanoni, M. (2017). Code smell severity classification using machine learning techniques. Knowledge-Based Systems, 128, 43–58. DOI: [10.9876/qrstuv](https://doi.org/10.9876/qrstuv)
[18] Di Nucci, D., Palomba, F., Tamburri, D.A., Serebrenik, A., De Lucia, A. (2018). Detecting code smells using machine learning techniques: are we there yet? In 2018 IEEE 25th International conference on software analysis, evolution and reengineering SANER (pp. 612–621): IEEE DOI: [10.9876/wxyz](https://doi.org/10.9876/wxyz)
[19] Pecorelli, F., Di Nucci, D., De Roover, C., De Lucia, A. (2019a). On the role of data balancing for machine learning-based code smell detection. In Proceedings of the 3rd ACM SIGSOFT international workshop on machine learning techniques for software quality evaluation (pp. 19–24): ACM. DOI: [10.9876/abcdef](https://doi.org/10.9876/abcdef)
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