COMPUTER ENGINEERING, INFORMATION SYSTEMS AND TECHNOLOGIES

Intelligent Decision Support System for Evaluating the Quality of Object-Oriented Codebases

Authors

V. A. Parashchenko
Sumy State University ROR
O. B. Berest
Sumy State University ROR
DOI https://doi.org/10.31649/

Keywords

object-oriented programming cohesion LCOM information-extreme intelligent technology recognition feature Kullback information criterion

Abstract

The paper presents the methodology and results of developing an intelligent decision support system (DSS) for the automated assessment of the architectural quality of object-oriented software. The proposed approach is based on information-extreme intelligent technology (IEIT), which provides the construction of an adaptive classifier of program classes according to code cohesion indicators. The developed system combines quantitative metrics, namely LCOM2–LCOM5 and YALCOM, with an information-based optimization criterion, which makes it possible to classify software components and explain the obtained results through metric values.

During the experiments on a dataset of one hundred Java classes, the optimal value of the information criterion E* = 0.55 was achieved, which corresponds to a high recognition accuracy. The system identified 78 classes with potential architectural defects and reduced the number of false-positive alerts compared with well-known static analyzers, such as SonarQube and PMD. The DSS can be integrated into a CI/CD environment for continuous code quality monitoring and provides explainable refactoring recommendations.

The application of the information-extreme approach demonstrated its efficiency in architectural quality assessment tasks; however, further research is required using larger datasets, hybrid models, and an extended set of software metrics.

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How to Cite

[1]
“Intelligent Decision Support System for Evaluating the Quality of Object-Oriented Codebases”, Вісник ВПІ, no. 4, pp. 8–17, Oct. 2026, doi: 10.31649/.

Author Biographies

V. A. Parashchenko, Sumy State University

Post-Graduate Student of the Chair of Computer Science

O. B. Berest, Sumy State University

Post-Graduate Student of the Chair of Computer Science

References

[1] A. Trautsch, S. Herbold, and J. Grabowski, “Are automated static analysis tools worth it?” Empirical Software Engineering, vol. 28, no. 3, p. 66, 2023. https://doi.org/10.1007/s10664-023-10301-2 .
[2] X. Liu, and G. Woo, “Using SonarQube to enhance the accuracy of programming assignment evaluation,” in Proc. INTED2019, 2019. https://doi.org/10.21125/inted.2019.1310 .
[3] S. A. Licorish, and M. Wagner, “Combining GIN and PMD for code improvements,” in Proc. GECCO Companion, 2022, pp. 790-793. https://doi.org/10.1145/3520304.3528772 .
[4] A. Trautsch, S. Herbold, and J. Grabowski, “Empirical analysis of static analysis efficiency,” Empirical Software Engineering, vol. 28, p. 66, 2023. https://doi.org/10.1007/s10664-023-10301-2
[5] M. Shen, A. Pillai, B. A. Yuan, J. C. Davis, and A. Machiry, “An empirical study on the use of static analysis tools in embedded software,” arXiv preprint arXiv:2310.00205, 2023. https://doi.org/10.48550/arXiv.2310.00205 .
[6] S. D. B. Effendi et al., “A language-agnostic framework for mining static analysis rules from code changes,” in Proc. ICSE-SEIP, 2023. https://doi.org/10.1109/ICSE-SEIP58684.2023.00035 .
[7] J.-M. Mineau and J.-F. Lalande, “Evaluating the reusability of Android static analysis tools,” in Proc. Int. Conf. Software Reuse, 2024, pp. 153-170, https://doi.org/10.1007/978-3-031-66459-5_10 .
[8] A. Ampatzoglou, et al., “Applying the Single Responsibility Principle in industry,” in Proc. EASE Conf., 2019, pp. 347-352. https://doi.org/10.1145/3319008.3320125 .
[9] G. Cairo, G. Carneiro, and M. Monteiro, “The impact of code smells on software bugs: A systematic review,” Information, vol. 9, no. 11, p. 273, 2018. https://doi.org/10.3390/info9110273 .
[10] J. Martins, C. Bezerra, A. Uchôa, and A. Garcia, “How do code smell co-occurrences removal impact internal quality attributes?” in Proc. Brazilian Symp. Software Engineering, 2021, pp. 54-63, https://doi.org/10.1145/3474624.3474642 .
[11] I. Chang, W. C. Choi, and I. C. Choi, “A systematic review of AIGC opportunities in programming,” in Proc. Int. Conf. Big Data and Education, 2024, pp. 29-35. https://doi.org/10.1145/3704289.3704301 .
[12] S. R. Chidamber, and C. F. Kemerer, “Towards a metrics suite for object-oriented design,” in Proc. OOPSLA, 1991, pp. 197-211. https://doi.org/10.1109/32.295895 .
[13] B. Henderson-Sellers, Object-Oriented Metrics: Measures of Complexity. Englewood Cliffs, NJ, USA: Prentice-Hall, 1995. [Online] Available: https://books.google.com.ua/books/about/Object_oriented_Metrics.html?id=j7sgAQAAIAAJ .
[14] M. Hitz, and B. Montazeri, “Measuring product attributes of object-oriented systems,” in Proc. ESEC, 1995, pp. 124–136. https://doi.org/10.1007/3-540-60406-5_11 .
[15] T. Sharma, and D. Spinellis, “Do we need improved code quality metrics?,” arXiv preprint arXiv:2012.12324, 2020. https://doi.org/10.48550/arXiv.2012.12324 .
[16] A. S. Dovbysh, and I. V. Shelekhov, Osnovy teorii rozpiznavannia obraziv. Sumy, Ukraine: SumDU, 2015. ISBN: 978-966-657-597-8. [Online] Available: https://document.kdu.edu.ua/info_zab/2824.pdf
[17] I. V. Shelekhov, “Information-extreme machine learning system for functional diagnostics,” Radioelectronics and Com
[18] A. S. Dovbysh, V. K. Obodiak, and I. V. Shelekhov, Suchasni informatsiini tekhnolohii v kiberbezpetsi. Sumy, Ukraine: SumDU, 2021. [Online] Available: https://essuir.sumdu.edu.ua/bitstream-download/123456789/82619/3/Obodiak_kiberbezpeka.pdf .

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