FIVE-FACTOR MATHEMATICAL MODEL FOR PROCESSING INFORMATION FROM CODE METRICS OF JAVA APPLICATIONS FOR ESTIMATING THEIR SIZE
DOI:
https://doi.org/10.35546/kntu2078-4481.2025.4.2.18Keywords:
mathematical model, іnformation processing, software code metric, Java, normalizing transformation, nonlinear regression, non-Gaussian data.Abstract
Reliable information processing from JAVA application code metrics at the early software design stages is crucial, because it directly impacts software development effort estimation. This paper proposes a mathematical model, specifically a five-factor nonlinear regression model, for the early code lines size estimation of JAVA applications. The object of the study is the process of information processing from code metrics of JAVA software applications. The subject of the study is a nonlinear regression model for information processing from code metrics of JAVA applications. The aim of the study is to increase the reliability of information processing from code metrics that are available on the UML class diagram for estimating the size parameter of JAVA applications at the early stages of software design by building a five-factor nonlinear regression mathematical model. To achieve this goal, data on software code metrics of 571 general open-source JAVA applications hosted on the GitHub platform were collected. The obtained dataset was randomly separated into training and test samples of 286 and 285 vectors, respectively. During data preprocessing for building the mathematical model, the parameter of the total number of classes and interfaces was separated into individual metrics for the first time. Additionally, average values of visible methods of classes and interfaces, class fields, and coupling between classes and interfaces were selected, which allowed avoiding multicollinearity during the regression model construction. Normalization of multidimensional data was performed using a six-dimensional normalizing Box-Cox transformation. The obtained mathematical model demonstrates better quality indicators, namely R2, MMRE, and PRED(0.25), compared to existing three-factor and fourfactor nonlinear regression models for information processing from JAVA application code metrics to estimate their size.
References
Boehm B., Abts C., Brown A.W., Chulani S., Clark B.K., Horowitz E., Madachy R., Reifer D., Steece B. Software cost estimation with COCOMO II. Prentice Hall. 2000.
Munialo S.W. A Review of Agile Software Effort Estimation Methods // International Journal of Computer Applications Technology and Research. Association of Technology and Science. 2016. Vol. 5. pp. 612–618. DOI:10.7753/IJCATR0509.1009.
Johnson J., Mulder H. Endless modernization: How infinite flow keeps software fresh. ResearchGate. 2021. URL: https://www.researchgate.net/publication/348849361_Endless_Modernization_How_Infinite_Flow_Keeps_Software_Fresh
The Standish Group. Chaos report 2015. 2015. URL: https://standishgroup.com/sample_research_files/CHAOSReport2015-Final.pdf
Oracle. Java. 2025. URL: https://www.oracle.com/java/
Cass S. Top programming languages 2024. IEEE Spectrum. 2024. URL: https://spectrum.ieee.org/topprogramming-languages-2024
Tan H.B.K., Zhao Y., Zhang H. Estimating LOC for information systems from their conceptual data models // Proceedings – International Conference on Software Engineering. 2006. pp. 321-330. DOI:10.1145/1134285.1134331.
Tan H.B.K., Zhao Y., Zhang H. Conceptual Data Model-Based Software Size Estimation for Information Systems // ACM Transactions of Software Engineering and Methodology. 2009. Vol. 19. DOI:10.1145/1571629.1571630.
Приходько Н.В., Приходько С.Б. Нелінійна регресійна модель для оцінювання розміру програмного забезпечення промислових інформаційних систем на Java // Моделювання та інформаційні технології. 2018. Вип. 85. С. 81–88. URL: http://nbuv.gov.ua/UJRN/Mtit_2018_85_14
Макарова Л.М., Приходько Н.В., Кудін О.О. Побудова нелінійної регресійної моделі для оцінювання розміру веб-додатків, реалізованих мовою Java // Вісник Херсонського національного технічного університету. 2019. № 2 (69). С. 145–153. URL: http://eir.nuos.edu.ua/handle/123456789/4443
Приходько С.Б., Приходько Н.В., Смикодуб Т.Г. Чотирьохфакторна нелінійна регресійна модель для оцінювання розміру JAVA-застосунків з відкритим кодом // Вчені записки ТНУ імені В.І. Вернадського. Серія: технічні науки Том 31 (70) № 2 Частина 1. 2020. С. 157–162. DOI:10.32838/2663-5941/2020.2-1/25
Орєхов О.С., Фаріонова Т.А. Математичні моделі для оцінювання розміру JAVA-застосунків // Вісник Херсонського національного технічного університету. 2024. Т. 89, № 2. С. 196–203. DOI:10.35546/kntu2078-4481.2024.2.28.
Oriekhov O., Farionova T., Chernova L. Three-factor nonlinear regression model of estimating the size of JAVAsoftware // Proceedings of the International Conference on Information Control Systems and Technologies (ICST-2024). 2024. pp. 506–518. URL: https://ceur-ws.org/Vol-3790/paper44.pdf
Орєхов О.С. Чотирьохфакторна нелінійна регресійна модель для оцінювання розміру JAVA-застосунків на ранніх стадіях розробки // Розвиток інформаційно-керуючих систем та технологій. 2024. С. 360–379. DOI:10.36059/978-966-397-422-4
Port D., Korte M. Comparative studies of the model evaluation criterions MMRE and PRED in software cost estimation research // Proceedings of the 2nd ACM-IEEE International Symposium on Empirical Software Engineering and Measurement, New York, 2008. pp. 51–60. DOI:10.1145/1414004.1414015
Subramanyam R., Krishnan M. Empirical analysis of CK metrics for object-oriented design complexity: Implications for software defects // IEEE Transactions on Software Engineering. 2003. Vol. 29, No. 4. pp. 297–310. DOI:10.1109/TSE.2003.1191795.
Olkin I., Sampson A.R. Multivariate Analysis: Overview // International encyclopedia of social & behavioral sciences (eds.) 1st edn., Elsevier, Pergamon, 2001. pp. 10240–10247
Mardia K. V., Measures of multivariate skewness and kurtosis with applications // Biometrika. 1970. Vol. 57. pp. 519–530. DOI:10.1093/biomet/57.3.519.
Prykhodko S., Prykhodko N., Mathematical Modeling of Non-Gaussian Dependent Random Variables by Nonlinear Regression Models Based on the Multivariate Normalizing Transformations // Mathematical Modeling and Simulation of Systems (MODS'2020). Advances in Intelligent Systems and Computing. 2021. Vol. 1265. PP. 166-174. DOI:10.1007/978-3-030-58124-4_16
Prykhodko S., Prykhodko N., Makarova L., Pukhalevych A. Outlier Detection in Non-Linear Regression Analysis Based on the Normalizing Transformations // 2020 IEEE 15th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET). Lviv-Slavske, Ukraine, 2020. pp. 407–410, DOI:10.1109/TCSET49122.2020.235464.
Lindsey C., Sheather S. Power transformation via multivariate Box–Cox // The Stata Journal. 2010. Vol. 10, No. 1. pp. 69–81. DOI:10.1177/1536867X1001000108.
Oriekhov O., Farionova T., Chernova L., Vorona M. A Five-Factor Nonlinear Regression Model for JAVA Applications Size Estimation // Proceedings of VI International Workshop on IT Project Management. 2025. Vol. 4023. pp. 1–10. URL: https://ceur-ws.org/Vol-4023/paper1.pdf
