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Genetic algorithms and heuristics hybridized for software architecture recovery

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Abstract

Large scale software systems must be decomposed into modular units to reduce maintenance efforts. Software Architecture Recovery (SAR) approaches have been introduced to analyze dependencies among software modules and automatically cluster them to achieve high modularity. These approaches employ various types of algorithms for clustering software modules. In this paper, we discuss design decisions and variations in existing genetic algorithms devised for SAR. We present a novel hybrid genetic algorithm that introduces three major differences with respect to these algorithms. First, it employs a greedy heuristic algorithm to automatically determine the number of clusters and enrich the initial population that is generated randomly. Second, it uses a different solution representation that facilitates an arithmetic crossover operator. Third, it is hybridized with a heuristic that improves solutions in each iteration. We present an empirical evaluation with seven real systems as experimental objects. We compare the effectiveness of our algorithm with respect to a baseline and state-of-the-art hybrid genetic algorithms. Our algorithm outperforms others in maximizing the modularity of the obtained clusters.

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Notes

  1. https://www.minitab.com/

  2. https://github.com/hasansozer/MOSARCH

References

  • Akbari, M., Izadkhah, H.: Hybrid of genetic algorithm and krill herd for software clustering problem. In: Proceedings of the 5th Conference on Knowledge Based Engineering and Innovation, pp 565–570 (2019)

  • Altinisik, M., Sozer, H.: Automated procedure clustering for reverse engineering PL/SQL programs. In: Proceedings of the 31st ACM Symposium on Applied Computing, pp 1440–1445 (2016)

  • Altinisik, M., Ersoy, E., Sozer, H.: Evaluating software architecture erosion for PL/SQL programs. In: Proceedings of the 11th European Conference on Software Architecture: Companion Proceedings, ACM, pp 159–165 (2017)

  • Andritsos, P., Tsaparas, P., Miller, R., et al.: LIMBO: Scalable clustering of categorical data. In: Proceedings of the 9th International Conference on Extending DataBase Technology, pp 531–532 (2004)

  • Barros, M.: An analysis of the effects of composite objectives in multiobjective software module clustering. In: Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation, pp 1205–1212 (2012)

  • Candela, I., Bavota, G., Russo, B., et al.: Using cohesion and coupling for software remodularization: Is it enough? ACM Transactions on Software Engineering and Methodology 25(3), 1–28 (2016)

    Article  Google Scholar 

  • Chen, C., Alfayez, R., Srisopha, K., et al.: Why is it important to measure maintainability, and what are the best ways to do it? In: Proceedings of the 39th International Conference on Software Engineering Companion, pp 377–378 (2017)

  • Clauset, A., Newman, M., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70(066), 111 (2004)

    Google Scholar 

  • Corazza, A., Martino, S.D., Maggio, V., et al.: Investigating the use of lexical information for software system clustering. In: Proceedings of the 15th European Conference on Software Maintenance and Reengineering, pp 35–44 (2011)

  • Desai, U., Bandyopadhyay, S., Tamilselvam, S.: Graph neural network to dilute outliers for refactoring monolith application. In: Proceedings of the 35th AAAI Conference on Artificial Intelligence, pp 72–80 (2021)

  • Doval, D., Mancoridis, A., Mitchell, B.: Automatic clustering of software systems using a genetic algorithm. In: Proceedings of the 9th International Workshop Software Technology and Engineering Practice, pp 73–81 (1999)

  • Ducasse, S., Pollet, D.: Software architecture reconstruction: A process-oriented taxonomy. IEEE Trans. Software Eng. 35(4), 573–591 (2009)

    Article  Google Scholar 

  • Eick, S., Graves, T., Karr, A., et al.: Does code decay? assessing the evidence from change management data. IEEE Trans. Software Eng. 27(1), 1–12 (2001)

    Article  Google Scholar 

  • Elyasi, M., Simitcioglu, M., Saydemir, A., et al.: HYGAR: A hybrid genetic algorithm for software architecture recovery. In: Proceedings of the 37th ACM Symposium on Applied Computing, pp 1417–1424 (2022)

  • Ersoy, E., Kaya, K., Altinisik, M., et al.: (2016) Using hypergraph clustering for software architecture reconstruction of data-tier software. In: Proceedings of the 10th European Conference on Software Architecture, pp 326–333

  • Garcia, J., Popescu, D., Mattmann, C., et al.: Enhancing architectural recovery using concerns. In: Proceedings of the 26th IEEE/ACM International Conference on Automated Software Engineering, pp 552–555 (2011)

  • Garcia, J., Krka, I., Mattmann, C., et al.: Obtaining ground-truth software architectures. In: Proceedings of the International Conference on Software Engineering, pp 901–910 (2013)

  • Garlan, D., Bachmann, F., Ivers, J., et al.: Documenting Software Architectures: Views and Beyond, 2nd edn. Addison-Wesley, Boston (2010)

    Google Scholar 

  • Gen, M., Cheng, R.: Genetic Algorithms and Engineering Optimization, vol. 7. John Wiley & Sons (2000)

    Google Scholar 

  • Goldberg, D.: Genetic Algorithms in Search, Optimization and Machine Learning, 1st edn. Addison-Wesley Longman Publishing Co. Inc., USA (1989)

    MATH  Google Scholar 

  • Harman, M., Yao, X., Praditwong, K.: Software module clustering as a multi-objective search problem. IEEE Trans. Software Eng. 37(02), 264–282 (2011)

    Article  Google Scholar 

  • Hendrickson, B., Leland, R.: A multi-level algorithm for partitioning graphs. In: Proceedings of the ACM/IEEE Conference on Supercomputing, p. 28 (1995)

  • Jalali, N.S., Izadkhah, H., Lofti, S.: Multi-objective search-based software modularization: structural and non-structural features. Soft. Comput. 23(21), 11,141-11,165 (2019)

    Article  Google Scholar 

  • Jin, W., Liu, T., Cai, Y., et al.: Service candidate identification from monolithic systems based on execution traces. IEEE Trans. Software Eng. 47(5), 987–1007 (2021)

    Article  Google Scholar 

  • Kalia, A., Xiao, J., Krishna, R., et al.: Mono2Micro: A practical and effective tool for decomposing monolithic Java applications to microservices. In: Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, p 1214–1224 (2021)

  • Karna, S.K., Sahai, R., et al.: An overview on taguchi method. International journal of engineering and mathematical sciences 1(1), 1–7 (2012)

    Google Scholar 

  • Karypis, G., Kumar, V.: A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM J. Sci. Comput. 20(1), 359–392 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  • Kruskal, W., Wallis, W.: Use of ranks in one-criterion variance analysis. J. Am. Stat. Assoc. 47(260), 583–621 (1952)

    Article  MATH  Google Scholar 

  • Kumari, A., Srinivas, K.: Hyper-heuristic approach for multi-objective software module clustering. J. Syst. Softw. 117(C), 384–401 (2016)

    Article  Google Scholar 

  • Lutellier, T., Chollak, D., Garcia, J., et al.: Measuring the impact of code dependencies on software architecture recovery techniques. IEEE Trans. Software Eng. 44(2), 159–181 (2018)

    Article  Google Scholar 

  • Maqbool, O., Babri, H.: The weighted combined algorithm: A linkage algorithm for software clustering. In: Proceedings of the 8th Euromicro Working Conference on Software Maintenance and Reengineering, pp 15–24 (2004)

  • Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. 2nd, Extended Ed.. Springer-Verlag, Berlin, Heidelberg (1994)

  • Mitchell, B., Mancoridis, S.: On the automatic modularization of software systems using the Bunch tool. IEEE Trans. Software Eng. 32(3), 193–208 (2006)

    Article  Google Scholar 

  • Mitchell, B., Mancoridis, S.: On the evaluation of the Bunch search-based software modularization algorithm. Soft. Comput. 12(1), 77–93 (2008)

    Article  Google Scholar 

  • Mkaouer, W., Kessentini, M., Shaout, A., et al.: Many-objective software remodularization using NSGA-III. ACM Transactions on Software Engineering and Methodology 24(3), 1–45 (2015)

    Article  Google Scholar 

  • Mohammadi, S., Izadkhah, H.: A new algorithm for software clustering considering the knowledge of dependency between artifacts in the source code. Inf. Softw. Technol. 105, 252–256 (2019)

    Article  Google Scholar 

  • Monçores, M., Alvim, A., Barros, M.: Large neighborhood search applied to the software module clustering problem. Comput. Oper. Res. 91, 92–111 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  • Mu, L., Sugumaran, V., Wang, F.: A hybrid genetic algorithm for software architecture re-modularization. Inf. Syst. Front. 22(5), 1133–1161 (2020)

    Article  Google Scholar 

  • Murphy, G., Notkin, D., Sullivan, K.: Software reflexion models: Bridging the gap between design and implementation. IEEE Trans. Software Eng. 27(4), 364–308 (2001)

    Article  Google Scholar 

  • Nicosia, V., Mangioni, G., Carchiolo, V., et al.: (2009) Extending the definition of modularity to directed graphs with overlapping communities. J. Stat. Mech: Theory Exp. 03, P03024 (2009)

    Google Scholar 

  • Nitin, V., Asthana, S., Ray, B., et al.: CARGO: ai-guided dependency analysis for migrating monolithic applications to microservices architecture. In: Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering, pp 20:1–20:12 (2022)

  • Noack, A., Rotta, R.: Multi-level algorithms for modularity clustering. In: Proceedings of the 8th International Symposium on Experimental Algorithms, pp 257–268 (2009)

  • Parnas, D.: On the criteria to be used in decomposing systems into modules. Commun. ACM 15(12), 1053–1058 (1972)

    Article  Google Scholar 

  • Rao, R.: Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int. J. Ind. Eng. Comput. 7(1), 19–34 (2016)

    Google Scholar 

  • Ross, P.: Taguchi Techniques for Quality Engineering: Loss Function, Orthogonal Experiments, Parameter and Tolerance Design, 2nd edn. McGraw-Hill, New York, NY (1996)

    Google Scholar 

  • Rossi, F., Villa-Vialaneix, N.: Représentation d’un grand réseau à partir d’une classification hiérarchique de ses sommets. Journal de la Société Française de Statistique 152(3), 34–65 (2011)

    MathSciNet  MATH  Google Scholar 

  • Sarhan, Q.I., Ahmed, B.S., Bures, M., et al.: Software module clustering: An in-depth literature analysis. IEEE Trans. Software Eng. 48(6), 1905–1928 (2022)

    Article  Google Scholar 

  • Saydemir, A., Simitcioglu, M., Sozer, H.: On the use of evolutionary coupling for software architecture recovery. In: Proceedings of the 15th Turkish National Software Engineering Symposium, pp 1–6 (2021)

  • Schuetz, P., Caflisch, A.: Efficient modularity optimization by multistep greedy algorithm and vertex mover refinement. Phys. Rev. E 77(046), 112 (2008)

    Google Scholar 

  • Sozer, H.: Evaluating the effectiveness of multi-level greedy modularity clustering for software architecture recovery. In: Proceedings of the 13th European Conference on Software Architecture, pp 71–87 (2019)

Download references

Funding

The work is supported by the Scientific and Research Council of Turkey under Grant No. 120E488

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Authors and Affiliations

  1. Computer Science Department, Özyeğin University, 34794, Istanbul, Turkey

    M. Esad Simitcioğlu, Abdullah Saydemir & Hasan Sözer

  2. Industrial Engineering Department, Özyeğin University, 34794, Istanbul, Turkey

    Milad Elyasi, Ali Ekici & O. Örsan Özener

  3. IMT Atlantique, LS2N-CNRS, La Chantrerie, 4 rue Alfred Kastler, 44300, Nantes, France

    Milad Elyasi

Authors
  1. Milad Elyasi
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  2. M. Esad Simitcioğlu
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  3. Abdullah Saydemir
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  4. Ali Ekici
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  5. O. Örsan Özener
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  6. Hasan Sözer
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Contributions

ME, AE, OOO and HS contributed to the conceptualization of the approach and development of the methodology. ME, AS and MES implemented the algorithms, conducted the experiments, and collected the results. All authors contributed to the writing of the original draft, and they all reviewed the final manuscript.

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Correspondence to Hasan Sözer.

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Elyasi, M., Simitcioğlu, M.E., Saydemir, A. et al. Genetic algorithms and heuristics hybridized for software architecture recovery. Autom Softw Eng 30, 19 (2023). https://doi.org/10.1007/s10515-023-00384-y

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