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Automated Software Engineering

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01.06.2024

`MIRROR`: multi-objective refactoring recommendation via correlation analysis

verfasst von: Yang Zhang, Ke Guan, Lining Fang

Erschienen in: Automated Software Engineering | Ausgabe 1/2024

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Abstract

Refactoring is a critical but complex process to improve code quality by altering software structure without changing the observable behavior. Search-based approaches have been proposed to recommend refactoring solutions. However, existing works tend to leverage all the sub-attributes in an objective and ignore the relationship between the sub-attributes. Furthermore, the types of refactoring operations in the existing works can be further augmented. To this end, this paper proposes a novel approach, called MIRROR, to recommend refactoring by employing a multi-objective optimization across three objectives: (i) improving quality, (ii) removing code smell, and (iii) maximizing the similarity to refactoring history. Unlike previous works, MIRROR provides a way to further optimize attributes in each objective. To be more specific, given an objective, MIRROR investigates the possible correlations among attributes and selects those attributes with low correlations as the representation of this objective. MIRROR is evaluated on 6 real-world projects by answering 6 research questions. The experimental results demonstrate that MIRROR recommends an average of 43 solutions for each project. Furthermore, we compare MIRROR against existing tools JMove and QMove, and show that the F1 of MIRROR is 5.63% and 3.75% higher than that of JMove and QMove, demonstrating the effectiveness of MIRROR.

Vorheriger Artikel DifFuzzAR: automatic repair of timing side-channel vulnerabilities via refactoring

Nächster Artikel Automatic recognizing relevant fragments of APIs using API references

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Alizadeh, V., Kessentini, M., Mkaouer, M.W., Ocinneide, M., Ouni, A., Cai, Y.: An interactive and dynamic search-based approach to software refactoring recommendations. IEEE Trans. Softw. Eng. 46(9), 932–961 (2020). https://doi.org/10.1109/TSE.2018.2872711CrossRef

Aniche, M.F., Maziero, E., Durelli, R.S., Durelli, V.H.S.: The effectiveness of supervised machine learning algorithms in predicting software refactoring, CoRR abs/2001.03338 (2020). arXiv:2001.03338

Antezana, A.S.: TOAD: a tool for recommending auto-refactoring alternatives, in: J. M. Atlee, T. Bultan, J. Whittle (Eds.), Proceedings of the 41st International Conference on Software Engineering: Companion Proceedings, ICSE 2019, Montreal, QC, Canada, May 25-31, 2019, IEEE ACM, pp. 174–176 (2019). https://doi.org/10.1109/ICSE-Companion.2019.00071

Apacheant: (2022). http://ant.apache.org/

Arcuri, A., Fraser, G.: Parameter tuning or default values? an empirical investigation in search-based software engineering. Empir. Softw. Eng. 18(3), 594–623 (2013). https://doi.org/10.1007/s10664-013-9249-9CrossRef

Bansiya, J., Davis, C.G.: A hierarchical model for object-oriented design quality assessment. IEEE Trans. Softw. Eng. 28(1), 4–17 (2002). https://doi.org/10.1109/32.979986CrossRef

Bell, D.: Software engineering: A programming approach, software engineering: a programming approach (2000)

Brown, W.J., Malveau, R.C., Mccormick, H.W., Mowbray, T.J.: Anti patterns: Refactoring software, architectures, and projects in crisis (1998)

Cinnéide, M. Ó., Tratt, L., Harman, M., Counsell, S., Moghadam, I.H.: Experimental assessment of software metrics using automated refactoring, in: P. Runeson, M. Höst, E. Mendes, A. A. Andrews, R. Harrison (Eds.), 2012 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement, ESEM ’12, Lund, Sweden - September 19 - 20, 2012, ACM, pp. 49–58 (2012). https://doi.org/10.1145/2372251.2372260

Couto, C.M.S., Rocha, H., Terra, R.: A quality-oriented approach to recommend move method refactorings, in: A. Malucelli, S. S. Reinehr (Eds.), Proceedings of the 17th Brazilian Symposium on Software Quality, SBQS 2018, Curitiba, Brazil, October 17-19, 2018, ACM, pp. 11–20 (2018). https://doi.org/10.1145/3275245.3275247

Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002). https://doi.org/10.1109/4235.996017CrossRef

Deb, K., Saxena, D.: Searching for pareto-optimal solutions through dimensionality reduction for certain large-dimensional multi-objective optimization problems. Comput. Intell. 56, 3352–3360 (2006)

Deb, K., Jain, H.: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part i: Solving problems with box constraints. IEEE Trans. Evol. Comput. 18(4), 577–601 (2014)CrossRef

Erlikh. L.: Leveraging legacy system dollars for e-business. IT Prof. 2(3), 17–23 (2000). https://doi.org/10.1109/6294.846201

Fokaefs, M., Tsantalis, N., Chatzigeorgiou, A.: Jdeodorant: Identification and removal of feature envy bad smells, in: 23rd IEEE International Conference on Software Maintenance (ICSM 2007), October 2-5, 2007, Paris, France, IEEE Computer Society, pp. 519–520 (2007). https://doi.org/10.1109/ICSM.2007.4362679

Foster, S.R., Griswold, W.G., Lerner, S.: Witchdoctor: IDE support for real-time auto-completion of refactorings, in: M. Glinz, G. C. Murphy, M. Pezzè (Eds.), 34th International Conference on Software Engineering, ICSE 2012, June 2-9, 2012, Zurich, Switzerland, IEEE Computer Society, pp. 222–232 (2012). https://doi.org/10.1109/ICSE.2012.6227191

Fowler, M.: Refactoring - improving the design of existing code (1997). URL http://martinfowler.com/books/refactoring.html

Ganttproject: (2022). www.ganttproject.biz

Griffith, I., Wahl, S., Izurieta, C.: Truerefactor: An automated refactoring tool to improve legacy system and application comprehensibility, in: 24th Intenational Conference on Computer Applications in Industry and Engineering 2011, Couer Science Deren, Monn Se Universiy, Honolulu, Hawaii, USA, pp. 316–321 (2011)

https://github.com/amir-zad-1/Java-QMOOD-Calculator. (2018)

Jfreechart: (2022). http://www.jfree.org/jfreechart/

Jhotdraw: (2022). http://www.jhotdraw.org/

Kessentini, W., Kessentini, M., Sahraoui, H.A., 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). https://doi.org/10.1109/TSE.2014.2331057CrossRef

Koza, J.R.: Genetic programming on the programming of computers by means of natural selection. MIT Press, Complex adaptive systems (1993)

Lin, Y., Peng, X., Cai, Y., Dig, D., Zheng, D., Zhao, W.: Interactive and guided architectural refactoring with search-based recommendation, in: T. Zimmermann, J. Cleland-Huang, Z. Su (Eds.), Proceedings of the 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering, FSE 2016, Seattle, WA, USA, November 13-18, 2016, ACM, pp. 535–546 (2016). https://doi.org/10.1145/2950290.2950317

Mariani, T., Vergilio, S.R.: A systematic review on search-based refactoring. Inf. Softw. Technol. 83, 14–34 (2017). https://doi.org/10.1016/j.infsof.2016.11.009CrossRef

Marinescu, C., Marinescu, R., Mihancea, P.F., Ratiu, D., Wettel, R.: iplasma: An integrated platform for quality assessment of object-oriented design, in: Proceedings of the 21st IEEE International Conference on Software Maintenance - Industrial and Tool volume, ICSM 2005, 25-30 September 2005, Budapest, Hungary, pp. 77–80 (2005)

Mkaouer, M.W., Kessentini, M., Bechikh, S., Deb, K., Cinnéide, M. Ó.: Recommendation system for software refactoring using innovization and interactive dynamic optimization, in: I. Crnkovic, M. Chechik, P. Grünbacher (Eds.), ACM/IEEE International Conference on Automated Software Engineering, ASE ’14, Vasteras, Sweden - September 15 - 19, ACM, 2014, pp. 331–336 (2014). https://doi.org/10.1145/2642937.2642965

Mkaouer, W., Kessentini, M., Shaout, A., Koligheu, P., Bechikh, S., Deb, K., Ouni, A.: Many-objective software remodularization using NSGA-III. ACM Trans. Softw. Eng. Methodol. (TOSEM) 24(3), 1–45 (2015). https://doi.org/10.1145/2729974CrossRef

Moha, N., Hacene, A.R., Valtchev, P., Guéhéneuc, Y.: Refactorings of design defects using relational concept analysis, in: R. Medina, S. A. Obiedkov (Eds.), Formal Concept Analysis, 6th International Conference, ICFCA 2008, Montreal, Canada, February 25-28, Proceedings, Vol. 4933 of Lecture Notes in Computer Science, Springer, 2008, pp. 289–304 (2008). https://doi.org/10.1007/978-3-540-78137-0_21

Moha, N., Guéhéneuc, Y., Duchien, L., Meur, A.L.: DECOR: A method for the specification and detection of code and design smells. IEEE Trans. Softw. Eng. 36(1), 20–36 (2010). https://doi.org/10.1109/TSE.2009.50CrossRef

Mohan, M., Greer, D.: Multirefactor: Automated refactoring to improve software quality, in: M. Felderer, D. M. Fernández, B. Turhan, M. Kalinowski, F. Sarro, D. Winkler (Eds.), Product-Focused Software Process Improvement - 18th International Conference, PROFES 2017, Innsbruck, Austria, November 29 - December 1, 2017, Proceedings, Vol. 10611 of Lecture Notes in Computer Science, Springer International Publishing, pp. 556–572 (2017). https://doi.org/10.1007/978-3-319-69926-4_46

Nan, W., Song, F., Li, X.: Quantum search-based software engineering: an exploratory study. Sci. Sin. Inf. 45(5), 623 (2015)

Ouni, A., Kessentini, M., Sahraoui, H.A., Boukadoum, M.: Maintainability defects detection and correction: a multi-objective approach. Autom. Softw. Eng. 20(1), 47–79 (2013). https://doi.org/10.1007/s10515-011-0098-8CrossRef

Ouni, A., Kessentini, M., Sahraoui, H.A., Hamdi, M.S.: The use of development history in software refactoring using a multi-objective evolutionary algorithm, in: C. Blum, E. Alba (Eds.), Genetic and Evolutionary Computation Conference, GECCO ’13, Amsterdam, The Netherlands, July 6-10, ACM, 2013, pp. 1461–1468 (2013). https://doi.org/10.1145/2463372.2463554

Ouni, A., Kessentini, M., Sahraoui, H.A., Inoue, K., Deb, K.: Multi-criteria code refactoring using search-based software engineering: an industrial case study. ACM Trans. Softw. Eng. Methodol. 25(3), 23–53 (2016). https://doi.org/10.1145/2932631CrossRef

Ouni, A., Kessentini, M., Cinnéide, M.Ó., Sahraoui, H.A., Deb, K., Inoue, K.: MORE: a multi-objective refactoring recommendation approach to introducing design patterns and fixing code smells. J. Softw. Evol. Process 29(5), e1843 (2017). https://doi.org/10.1002/smr.1843CrossRef

Ouni, A.: A multi-objective refactoring approach to introduce design patterns and fix anti-patterns (2015)

O’Keeffe, M., Cinnéide, M.Ó.: Search-based refactoring: an empirical study. J. Softw. Maint. Res. Pract. 20(5), 345–364 (2008). https://doi.org/10.1002/smr.378CrossRef

Palomba, F., Bavota, G., Penta, M.D., Oliveto, R., Poshyvanyk, D., Lucia, A.D.: Mining version histories for detecting code smells. IEEE Trans. Softw. Eng. 41(5), 462–489 (2015). https://doi.org/10.1109/TSE.2014.2372760CrossRef

Praditwong, K., Harman, M., Yao, X.: Software module clustering as a multi-objective search problem. IEEE Trans. Softw. Eng. 37(2), 264–282 (2011). https://doi.org/10.1109/TSE.2010.26CrossRef

Rebai, S., Kessentini, M., Alizadeh, V., Sghaier, O.B., Kazman, R.: Recommending refactorings via commit message analysis. Inf. Softw. Technol. 126, 106332 (2020). https://doi.org/10.1016/j.infsof.2020.106332CrossRef

Rhino: (2022). http://www.mozilla.org/rhino/

Silva, D., Terra, R., Valente, M.T.: Jextract: An eclipse plug-in for recommending automated extract method refactorings (2015). arXiv: abs/1506.06086

Terra, R., Valente, M.T., Miranda, S., Sales, V.: Jmove: A novel heuristic and tool to detect move method refactoring opportunities. J. Syst. Softw. 138, 19–36 (2018). https://doi.org/10.1016/j.jss.2017.11.073CrossRef

Tsantalis, N., Mansouri, M., Eshkevari, L.M., Mazinanian, D., Dig, D.: Accurate and efficient refactoring detection in commit history, in: M. Chaudron, I. Crnkovic, M. Chechik, M. Harman (Eds.), Proceedings of the 40th International Conference on Software Engineering, ICSE 2018, Gothenburg, Sweden, May 27 - June 03, 2018, ACM, pp. 483–494 (2018). https://doi.org/10.1145/3180155.3180206

Ujihara, N., Ouni, A., Ishio, T., Inoue, K.: c-jrefrec: Change-based identification of move method refactoring opportunities, in: M. Pinzger, G. Bavota, A. Marcus (Eds.), IEEE 24th International Conference on Software Analysis, Evolution and Reengineering, SANER 2017, Klagenfurt, Austria, February 20-24, 2017, IEEE Computer Society, pp. 482–486 (2017). https://doi.org/10.1109/SANER.2017.7884658

Wang, X., Wang, Y.: The objective relation analysis method for multiobjective decision making and analysis. Syst. Eng. Theory Pract. 20(12), 63–66 (2000). https://doi.org/10.12011/1000-6788(2000)12-63

Xerces-j: (2022). http://xerces.apache.org/xerces-j/

Zanetti, M.S., Tessone, C.J., Scholtes, I., Schweitzer, F.: Automated software remodularization based on move refactoring: a complex systems approach, in: W. Binder, E. Ernst, A. Peternier, R. Hirschfeld (Eds.), 13th International Conference on Modularity, MODULARITY ’14, Lugano, Switzerland, April 22-26, ACM, 2014, pp. 73–84 (2014). https://doi.org/10.1145/2577080.2577097

Zhang, Y., Dong, C., Liu, H., Ge, C.: Code smell detection approach based on pre-training model and multi-level information. J. Softw. 005, 033 (2022a)

Zhang, Y., Ge, C., Hong, S., Tian, R., Dong, C., Liu, J.: Delesmell: Code smell detection based on deep learning and latent semantic analysis. Knowl. Based Syst. 255, 109737 (2022b). https://doi.org/10.1016/j.knosys.2022.109737CrossRef

Zhao, Y., Yang, Y., Zhou, Y., Ding, Z.: DEPICTER: a design-principle guided and heuristic-rule constrained software refactoring approach. IEEE Trans. Reliab. 71(2), 698–715 (2022). https://doi.org/10.1109/TR.2022.3159851CrossRef

Titel: MIRROR: multi-objective refactoring recommendation via correlation analysis
verfasst von: Yang Zhang
Ke Guan
Lining Fang
Publikationsdatum: 01.06.2024
Verlag: Springer US
Erschienen in: Automated Software Engineering / Ausgabe 1/2024
Print ISSN: 0928-8910
Elektronische ISSN: 1573-7535
DOI: https://doi.org/10.1007/s10515-023-00400-1

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