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Enhanced Service Point Approach for Microservices Based Applications Using Machine Learning Techniques

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Advanced Informatics for Computing Research (ICAICR 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1575))

Abstract

The migration of service oriented architecture (SOA) based applications to microservices architecture is a current research trend in the domain of software engineering. Estimating the effort required for migration is a challenging task as the traditional methods are not suitable for this new architectural style of microservices. Service Points (SP) is one new approach proposed by us for estimating the effort required for the migration of SOA based applications to microservices architecture. However, the use of machine learning techniques gives promising benefits in software effort estimation. To improve the accuracy of the service points approach, multiple regression analysis with the Leave-N-Out policy is applied. The standard service points approach, service points approach with Karner’s ratings and proposed machine learning based approach are considered for comparison with actual efforts of the chosen dataset of applications. The accuracy of the models is evaluated using different measures such as MRE, RMSE, MAE, etc. It is clear that the effort estimation using regression analysis gives higher accuracy. Using machine learning techniques improves the accuracy of the effort estimation and helps software architects in better planning and execution of the migration process.

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References

  1. Thönes, J.: Microservices. IEEE Softw. 32(1), 116 (2015)

    Article  Google Scholar 

  2. Raj V, Ravichandra S. Microservices: a perfect SOA based solution for enterprise applications compared to web services. In: 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), pp. 1531–1536. IEEE, 18 May 2018

    Google Scholar 

  3. Salah, T., Zemerly, M.J., Yeun, C.Y., Al-Qutayri, M., Al-Hammadi, Y.: The evolution of distributed systems towards microservices architecture. In: 2016 11th International Conference for Internet Technology and Secured Transactions (ICITST), pp. 318–325. IEEE, 5 December 2016

    Google Scholar 

  4. Raj, V., Sadam, R.: Patterns for migration of SOA based applications to microservices architecture. J. Web Eng. 10, 1229–46 (2021)

    Google Scholar 

  5. Raj, V., Sadam, R.: Evaluation of SOA-based web services and microservices architecture using complexity metrics. SN Comput. Sci. 2(5), 1–10 (2021). https://doi.org/10.1007/s42979-021-00767-6

    Article  Google Scholar 

  6. Taibi, D., Lenarduzzi, V., Pahl, C.: Processes, motivations, and issues for migrating to microservices architectures: an empirical investigation. IEEE Cloud Comput. 4(5), 22–32 (2017)

    Article  Google Scholar 

  7. Soldani, J., Tamburri, D.A., Van Den Heuvel, W.J.: The pains and gains of microservices: a systematic grey literature review. J. Syst. Softw. 1(146), 215–232 (2018)

    Article  Google Scholar 

  8. Pendharkar, P.C., Subramanian, G.H., Rodger, J.A.: A probabilistic model for predicting software development effort. IEEE Trans. Softw. Eng. 31(7), 615–24 (2005)

    Article  Google Scholar 

  9. Subramanian, G.H., Pendharkar, P.C., Wallace, M.: An empirical study of the effect of complexity, platform, and program type on software development effort of business applications. Empirical Softw. Eng. 11(4), 541–53 (2006)

    Article  Google Scholar 

  10. Canfora, G., Fasolino, A.R., Frattolillo, G., Tramontana, P.: A wrapping approach for migrating legacy system interactive functionalities to service oriented architectures. J. Syst. Softw. 81(4), 463–80 (2008)

    Article  Google Scholar 

  11. Siddiqui, Z.A., Tyagi, K.: A critical review on effort estimation techniques for service-oriented-architecture-based applications. Int. J. Comput. Appl. 38(4), 207–16 (2016)

    Google Scholar 

  12. Raj, V., Ravichandra, S.: A novel effort estimation approach for migration of SOA applications to microservices. J. Inf. Syst. Telecommun. (JIST). 3(36) (2021)

    Google Scholar 

  13. Sehra, S.K., Brar, Y.S., Kaur, N., Sehra, S.S.: Research patterns and trends in software effort estimation. Inf. Softw. Technol. 1(91), 1–21 (2017)

    Article  Google Scholar 

  14. Wen, J., Li, S., Lin, Z., Hu, Y., Huang, C.: Systematic literature review of machine learning based software development effort estimation models. Inf. Softw. Technol. 54(1), 41–59 (2012)

    Article  Google Scholar 

  15. Karner, G.: Resource estimation for objectory projects. Objective Syst. SF AB. 17(17), 1–9 (1993)

    Google Scholar 

  16. Montgomery, D.C., Peck, E.A., Vining, G.G.: Introduction to Linear Regression Analysis. Wiley, Hoboken 9 Apr 2012

    Google Scholar 

  17. Shepperd, M., Schofield, C.: Estimating software project effort using analogies. IEEE Trans. Softw. Eng. 23(11), 736–43 (1997)

    Article  Google Scholar 

  18. Sarro, F., Petrozziello, A., Harman, M.: Multi-objective software effort estimation. In: 2016 IEEE/ACM 38th International Conference on Software Engineering (ICSE), pp. 619–630. IEEE, 14 May 2016

    Google Scholar 

  19. Munialo, S.W., Wanjala, S.: A size metric-based effort estimation method for service oriented architecture systems (Doctoral dissertation, MMUST) (2020)

    Google Scholar 

  20. Menzies, T., Yang, Y., Mathew, G., Boehm, B., Hihn, J.: Negative results for software effort estimation. Empirical Softw. Eng. 22(5), 2658–2683 (2016). https://doi.org/10.1007/s10664-016-9472-2

    Article  Google Scholar 

  21. Port, D., Korte, M.: Comparative studies of the model evaluation criterions MMRE and pred in software cost estimation research. In: Proceedings of the Second ACM-IEEE International Symposium on Empirical Software Engineering and Measurement, pp. 51–60, 9 October 2008

    Google Scholar 

  22. Raj, V., Sadam, R.: Performance and complexity comparison of service oriented architecture and microservices architecture. Int. J. Commun. Netw. Distrib. Syst. 27(1), 100–17 (2021)

    Google Scholar 

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Authors and Affiliations

  1. BVRIT Hyderabad College of Engineering for Women, Telangana, India

    Vinay Raj

  2. National Institute of Technology Warangal, Telangana, India

    Sadam Ravichandra

Authors
  1. Vinay Raj
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  2. Sadam Ravichandra
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Corresponding author

Correspondence to Vinay Raj .

Editor information

Editors and Affiliations

  1. Papua New Guinea University of Technology, Lae, Papua New Guinea

    Ashish Kumar Luhach

  2. Namibia University of Science and Technology, Windhoek, Namibia

    Dharm Singh Jat

  3. Universiti Malaysia Pahang, Pekan, Malaysia

    Kamarul Bin Ghazali Hawari

  4. University of Eastern Finland, Kuopio, Finland

    Xiao-Zhi Gao

  5. Saint Mary's University, Halifax, NS, Canada

    Pawan Lingras

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Raj, V., Ravichandra, S. (2022). Enhanced Service Point Approach for Microservices Based Applications Using Machine Learning Techniques. In: Luhach, A.K., Jat, D.S., Hawari, K.B.G., Gao, XZ., Lingras, P. (eds) Advanced Informatics for Computing Research. ICAICR 2021. Communications in Computer and Information Science, vol 1575. Springer, Cham. https://doi.org/10.1007/978-3-031-09469-9_7

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  • DOI: https://doi.org/10.1007/978-3-031-09469-9_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-09468-2

  • Online ISBN: 978-3-031-09469-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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