Comparison of solutions to the task of IT product configuration items early identification using hierarchical clusterization methods

Authors

DOI:

https://doi.org/10.15587/1729-4061.2024.303526

Keywords:

information system, configuration item, hierarchical clustering, Chameleon method, Chebyshev distance

Abstract

The object of this study is the IT project configuration management process.

During the research, the problem of early identification of configuration items (CI) in the information system (IS) of enterprise management was solved. Research in this field is mainly aimed at solving the task of early identification of services and microservices during the refactoring of software systems. The issue of the application of artificial intelligence methods for the detection of CI has not been sufficiently investigated.

During the study, the Chameleon hierarchical clustering method was adapted to solve the problem of early identification of CI IS. This method takes into account both the internal similarity and the connectivity of individual functions of the studied IS.

The adapted Chameleon method was used when solving the task of early identification of CI in the functional task "Formation and maintenance of an individual plan of a scientific and pedagogical employee of the department". 10 functions and 12 essences of the problem database were considered as the initial CIs. The result of the solution is a dendrogram with all possible options for decomposition of the description of the task architecture into individual CIs.

Based on the results, a comparative analysis of the use of Chameleon, DIANA, and AGNES methods for solving the problem of early identification was carried out. According to the criteria "Number of vertices of the dendrogram", "Number of levels of decomposition of the dendrogram", and "Evenness of filling the elements of the dendrogram", the results from using the Chameleon method are the best.

Using the research results allows automating the procedure of forming backlogs of IT project implementation teams. This makes it possible to improve the quality of IS development by assigning IS containing similar functions to the same IT project executor

Author Biographies

Maksym Ievlanov, Kharkiv National University of Radio Electronics

Doctor of Technical Sciences, Professor

Department of Information Control System

Nataliya Vasiltcova, Kharkiv National University of Radio Electronics

PhD, Associate Professor

Department of Information Control System

Iryna Panforova, Kharkiv National University of Radio Electronics

PhD, Associate Professor

Department of Information Control System

Borys Moroz, Dnipro University of Technology

Doctor of Technical Sciences, Professor

Department of Software Engineering

Andrii Martynenko, Dnipro University of Technology

PhD

Department of Software Engineering

Dmytro Moroz, Dnipro University of Technology

PhD

Department of Software Engineering

References

  1. Bourque, P., Fairley, R. E. (Eds.) (2014). Guide to the Software Engineering Body of Knowledge. Version 3.0. IEEE Computer Society, 335. Available at: https://cs.fit.edu/~kgallagher/Schtick/Serious/SWEBOKv3.pdf
  2. Quigley, J. M., Robertson, K. L. (2019). Configuration Management. Auerbach Publications. https://doi.org/10.1201/9780429318337
  3. -2015 - ISO/IEC/IEEE International Standard - Systems and software engineering -- System life cycle processes. https://doi.org/10.1109/ieeestd.2015.7106435
  4. Farayola, O. A., Hassan, A. O., Adaramodu, O. R., Fakeyede, O. G., Oladeinde, M. (2023). Configuration management in the modern era: best practices, innovations, and challenges. Computer Science & IT Research Journal, 4 (2), 140–157. https://doi.org/10.51594/csitrj.v4i2.613
  5. Reiff-Marganiec, S., Tilly, M. (Eds.) (2012). Handbook of Research on Service-Oriented Systems and Non-Functional Properties. IGI Global. https://doi.org/10.4018/978-1-61350-432-1
  6. Cadavid, H., Andrikopoulos, V., Avgeriou, P., Broekema, P. C. (2022). System and software architecting harmonization practices in ultra-large-scale systems of systems: A confirmatory case study. Information and Software Technology, 150, 106984. https://doi.org/10.1016/j.infsof.2022.106984
  7. Faitelson, D., Heinrich, R., Tyszberowicz, S. (2017). Supporting Software Architecture Evolution by Functional Decomposition. Proceedings of the 5th International Conference on Model-Driven Engineering and Software Development. https://doi.org/10.5220/0006206204350442
  8. Shahin, R. (2021). Towards Assurance-Driven Architectural Decomposition of Software Systems. Computer Safety, Reliability, and Security. SAFECOMP 2021 Workshops, 187–196. https://doi.org/10.1007/978-3-030-83906-2_15
  9. Suljkanović, A., Milosavljević, B., Inđić, V., Dejanović, I. (2022). Developing Microservice-Based Applications Using the Silvera Domain-Specific Language. Applied Sciences, 12 (13), 6679. https://doi.org/10.3390/app12136679
  10. Felfernig, A., Le, V.-M., Popescu, A., Uta, M., Tran, T. N. T., Atas, M. (2021). An Overview of Recommender Systems and Machine Learning in Feature Modeling and Configuration. Proceedings of the 15th International Working Conference on Variability Modelling of Software-Intensive Systems. https://doi.org/10.1145/3442391.3442408
  11. Abolfazli, A., Spiegelberg, J., Palmer, G., Anand, A. (2023). A Deep Reinforcement Learning Approach to Configuration Sampling Problem. 2023 IEEE International Conference on Data Mining (ICDM). https://doi.org/10.1109/icdm58522.2023.00009
  12. Sellami, K., Saied, M. A., Ouni, A. (2022). A Hierarchical DBSCAN Method for Extracting Microservices from Monolithic Applications. The International Conference on Evaluation and Assessment in Software Engineering 2022. https://doi.org/10.1145/3530019.3530040
  13. Krause, A., Zirkelbach, C., Hasselbring, W., Lenga, S., Kroger, D. (2020). Microservice Decomposition via Static and Dynamic Analysis of the Monolith. 2020 IEEE International Conference on Software Architecture Companion (ICSA-C). https://doi.org/10.1109/icsa-c50368.2020.00011
  14. Ievlanov, M., Vasiltcova, N., Neumyvakina, O., Panforova, I. (2022). Development of a method for solving the problem of it product configuration analysis. Eastern-European Journal of Enterprise Technologies, 6 (2 (120)), 6–19. https://doi.org/10.15587/1729-4061.2022.269133
  15. Karypis, G., Han, E.-H., Kumar, V. (1999). Chameleon: hierarchical clustering using dynamic modeling. Computer, 32 (8), 68–75. https://doi.org/10.1109/2.781637
  16. Han, J., Kamber, M., Pei, J. (2012). Data Mining: Concepts and Techniques. Morgan Kaufmann. https://doi.org/10.1016/c2009-0-61819-5
  17. Vasyltsova, N. V., Panforova, I. Yu. (2022). Doslidzhennia vykorystannia metodiv ierarkhichnoi klasteryzatsiyi pid chas vyrishennia zadachi analizu konfihuratsiyi IT-produktu. ASU ta prylady avtomatyky, 178, 37–49. Available at: https://www.ewdtest.com/asu/wp-content/uploads/2024/01/ASUiPA_178_37_49.pdf
Comparison of solutions to the task of IT product configuration items early identification using hierarchical clusterization methods

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Published

2024-06-28

How to Cite

Ievlanov, M., Vasiltcova, N., Panforova, I., Moroz, B., Martynenko, A., & Moroz, D. (2024). Comparison of solutions to the task of IT product configuration items early identification using hierarchical clusterization methods. Eastern-European Journal of Enterprise Technologies, 3(2 (129), 20–33. https://doi.org/10.15587/1729-4061.2024.303526