DOI: https://doi.org/10.32515/2664-262X.2025.11(42).1.5-13
Knowledge Representation Model for the Complexity Analysis of the Software Development using Saas/Paas Platforms
About the Authors
Dmytro Bilous, post-graduate, Vinnytsia National Technical University, Vinnytsia, Ukraine, e-mail: dmytro.bilous@gmail.com, ORCID ID: 0009-0007-6625-6761
Andrii Kozlovskyi, Associate Professor, PhD in Technics (Candidate of Technics Sciences), Vinnytsia National Technical University, Vinnytsia, Ukraine, e-mail: akozlovskyi@vntu.edu.ua, ORCID ID: 0000-0001-9697-1511
Abstract
A critical factor in successful project management and effective planning is the analysis of software development complexity and effort. The use of industry-standard methods, such as Function Point Analysis, serves as an effective means of improving estimation accuracy while simultaneously reducing the cost of the estimation process itself. However, standard methods, most of which were developed several decades ago, are not sufficiently adapted to modern realities, including agile software development and the use of pre-built or standardized solutions. This study proposes knowledge representation model that combine production and frame-based approaches to address the challenge of assessing the complexity and effort of software development involving SaaS (Software as a Service) and PaaS (Platform as a Service) solutions. A knowledge base model has been developed, integrating both frame-based and production models while ensuring compatibility with Function Point Analysis.
The developed models and their interactions can serve as the foundation for a decision-making model within an information technology framework for software development complexity assessment, considering environmental factors and evaluation criteria. The application of the proposed models will enable the creation of automated algorithms for software development complexity estimation under conditions of incomplete functional requirements.
The proposed approach improves effort estimation by considering real-world implementation contexts, which is particularly relevant for modern IT projects. Additionally, the combination of frame-based and production models lays the groundwork for further integration with artificial intelligence and machine learning to automate effort estimation processes.
Future research should focus on refining the decision-making model, conducting experimental validation against traditional methods, and expanding its capabilities using fuzzy logic and neural networks for dynamic complexity assessment. The integration of this approach into decision support systems (DSS) for software project and resource management also remains a promising direction.
Keywords
software development complexity analysis, knowledge representation model, frame-based model, production rule model, function point analysis
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References
1. de Freitas Junior, M., Fantinato, M., & Sun, V. (2015). Improvements to the Function Point Analysis Method: A Systematic Literature Review. IEEE Transactions on Engineering Management, 62(4), 495-506. https://doi.org/10.1109/TEM.2015.2453354.
2. Lavazza, L., Locoro, A., Liu, G., & Meli, R. (2023). Estimating software functional size via machine learning. ACM Transactions on Software Engineering and Methodology, 32(5), Article 114. https://doi.org/10.1145/3582575.
3. Hoc, H. T., Hai, V. V., & Nhung, H. L. T. K. (2021). An approach to adjust effort estimation of function point analysis. Software Engineering and Algorithms, 522-537. https://doi.org/10.1007/978-3-030-77442-4_45.
4. Sree, P. R., & Ramesh, S. N. S. V. S. C. (2016). Improving efficiency of fuzzy models for effort estimation by cascading & clustering techniques. Procedia Computer Science, 85, 278-285. https://doi.org/10.1016/j.procs.2016.05.234.
5. Nwaiwu, J. C., & Oluwadare, S. (2016). Analytic study of fuzzy-based model for software cost estimation. 2nd ACM International Conference on Computing Research and Innovations (CoRI). https://www.researchgate.net/publication/313351062_Analytic_Study_of_Fuzzy-based_model_for_Software_Cost_Estimation.
6. Setyohadi, D. B. (2019). Estimation of software development project success using fuzzy logics. Advances in Science, Technology and Engineering Systems Journal, 4(2), 280-287. https://doi.org/10.25046/aj040236
7. Xia, W., Ho, D., Capretz, L. F., & Ahmed, F. (2019). Updating weight values for function point counting. International Journal of Hybrid Intelligent Systems, 6(1), 1-14. https://doi.org/10.48550/arXiv.2005.11218.
8. Kaushik, A., Soni, A., & Soni, R. (2013). A type-2 fuzzy logic based framework for function points. International Journal of Intelligent Systems and Applications, 5(3), 74-82. doi.org/10.5815/ijisa.2013.03.08.
9. Bou Nassif, A., Azzeh, M., Idri, A., & Abran, A. (2019). Software development effort estimation using regression fuzzy models. Computational Intelligence and Neuroscience Journal. doi.org/10.48550/arXiv.1902.03608.
10. Mohamed, M., Emam, O., & Azzam, S. M. (2024). Software cost estimation prediction using a convolutional neural network and particle swarm optimization algorithm. Scientific Reports, 14, Article 13129. https://doi.org/10.1038/s41598-024-63025-8.
11. Malik, G., Cevik, M., Bera, S., & Yildirim, S. (2022). Software requirement specific entity extraction using transformer models. The 35th Canadian Conference on Artificial Intelligence. doi.org/10.21428/594757db.9e433d7c.
12. Bilous, D.A., & Kozlovskyi, A.V. (2024). Using function point analysis for professional service and maintenance IT projects: A tailoring approach for enhanced size and effort estimation. XI International Scientific and Practical Conference «Modern Science: Theoretical and Practical View», April 16-17, 2024, Madrid, Spain, 82-90 [in Ukrainian]. https://doi.org/10.5281/zenodo.11065612.
13. Bilous, D., & Kozlovskyi, A. (2025). Improving the efficiency of assessing the functional complexity of software development and support using fuzzy logic models. Youth in Science: Research, Problems, Prospects (МН-2025). https://conferences.vntu.edu.ua/index.php/mn/mn2025/paper/view/22288 [in Ukrainian]
14. Bilous, D., & Kozlovskyi, A. (2025). A frame knowledge model for fuzzy analysis of functional points in modern development using SaaS technologies. LIV All-Ukrainian Scientific and Technical Conference of the Faculty of Intellectual Information Technologies and Automation. https://conferences.vntu.edu.ua/ index.php/all-fksa/all-fksa-2025/paper/view/23168 [in Ukrainian].
15. Yukhymchuk, S.V. (2003). Mathematical risk models for decision support systems. Vinnytsia: Universum-Vinnytsia [in Ukrainian].
Citations
1. de Freitas Junior M., Fantinato M., Sun, V. Improvements to the Function Point Analysis Method: A Systematic Literature Review. IEEETransactions on Engineering Management. 2015. Vol. 62, №4, P. 495-506. URL: https://doi.org/10.1109/TEM.2015.2453354 (дата звернення: 22.02.2025).
2. Lavazza L., Locoro A., Liu G. Estimating software functional size via machine learning. ACM Transactions on Software Engineering and Methodology. 2023. Vol. 32, №5. URL: https://doi.org/10.1145/3582575 (дата звернення: 22.02.2025).
3. Hoc H. T., Hai V. V., Nhung H. L. T. K. An approach to adjust effort estimation of function point analysis. Software Engineering and Algorithms. 2021. P. 522-537. URL: https://doi.org/10.1007/978-3-030-77442-4_45 (дата звернення: 22.02.2025).
4. Sree P. R., Ramesh S. N. S. V. S. C. Improving efficiency of fuzzy models for effort estimation by cascading & clustering techniques. Procedia Computer Science. 2016. Vol. 85, P. 278-285. URL: https://doi.org/10.1016/j.procs.2016.05.234 (дата звернення: 22.02.2025).
5. Nwaiwu J. C., Oluwadare S. Analytic study of fuzzy-based model for software cost estimation. 2nd ACM International Conference on Computing Research and Innovations (CoRI). 2016. Vol. 1755. URL: https://www.researchgate.net/publication/313351062_Analytic_Study_of_Fuzzy-based_model_for_Software_Cost_Estimation (дата звернення: 22.02.2025)
6. Setyohadi D. B. Estimation of software development project success using fuzzy logics. Advances in Science, Technology and Engineering Systems Journal. 2019. Vol. 4, №2, P. 280-287. URL: https://doi.org/10.25046/aj040236 (дата звернення: 22.02.2025).
7. Xia W., Ho D., Capretz L. F. Updating weight values for function point counting. International Journal of Hybrid Intelligent Systems. 2019, Vol. 6, №1, P. 1-14. URL: https://doi.org/10.48550/arXiv.2005.11218 (дата звернення: 22.02.2025).
8. Kaushik A., Soni A., Soni R. A type-2 fuzzy logic based framework for function points. International Journal of Intelligent Systems and Applications, 2013, Vol. 5, №3, P. 74-82. URL: https://doi.org/10.5815/ijisa.2013.03.08 (дата звернення: 22.02.2025).
9. Bou Nassif A., Azzeh M., Idri A. Software development effort estimation using regression fuzzy models. Computational Intelligence and Neuroscience Journal. 2019. URL: https://doi.org/10.48550/arXiv.1902.03608 (дата звернення: 22.02.2025).
10. Mohamed M., Emam O., Azzam S. M. Software cost estimation prediction using a convolutional neural network and particle swarm optimization algorithm. Scientific Reports. 2024, Vol.14, URL: https://doi.org/10.1038/s41598-024-63025-8 (Дата звернення: 22.02.2025).
11. Malik G., Cevik M., Bera, S. Software requirement specific entity extraction using transformer models. The 35th Canadian Conference on Artificial Intelligence. 2024. URL: https://doi.org/10.21428/594757db.9e433d7c (дата звернення: 22.02.2025)
12. Білоус Д.А., Козловський, А.В. Використання аналізу функціональних точок для ІТ-проєктів підтримки та професійного сервісу: модифікований підхід для покращення оцінки розміру та витрат. XI Міжнародна науково-практична конференція «Сучасна наука: теоретичний та практичний погляд», 16-17 квітня 2024, Мадрид, Іспанія, C.82-90. URL: https://doi.org/10.5281/zenodo.11065612 (дата звернення: 22.02.2025).
13. Білоус Д.А., Козловський, А.В. Підвищення ефективності оцінки функціональної складності розробки та підтримки програмного забезпечення з використанням моделей нечіткої логіки. Молодь в науці: дослідження, проблеми, перспективи (МН-2025). 2025. URL: https://conferences.vntu.edu.ua/index.php/mn/mn2025/paper/view/22288 (дата звернення: 22.02.2025).
14. Білоус Д.А., Козловський А.В. Фреймова модель знань для нечіткого аналізу функціональних точок у сучасній розробці з використанням SaaS технологій. LIV Всеукраїнська науково-технічна конференція факультету інтелектуальних інформаційних технологій та автоматизації. 2025. URL: https://conferences.vntu.edu.ua/index.php/all-fksa/all-fksa-2025/paper/view/23168 (дата звернення: 22.02.2025).
15. Юхимчук С. В. Математичні моделі ризику для систем підтримки прийняття рішень: монографія. Вінниця: УНІВЕРСУМ-Вінниця. 2003. 188 с.
Copyright (c) 2025 Dmytro Bilous, Andrii Kozlovskyi
Knowledge Representation Model for the Complexity Analysis of the Software Development using Saas/Paas Platforms
About the Authors
Dmytro Bilous, post-graduate, Vinnytsia National Technical University, Vinnytsia, Ukraine, e-mail: dmytro.bilous@gmail.com, ORCID ID: 0009-0007-6625-6761
Andrii Kozlovskyi, Associate Professor, PhD in Technics (Candidate of Technics Sciences), Vinnytsia National Technical University, Vinnytsia, Ukraine, e-mail: akozlovskyi@vntu.edu.ua, ORCID ID: 0000-0001-9697-1511
Abstract
Keywords
Full Text:
PDFReferences
1. de Freitas Junior, M., Fantinato, M., & Sun, V. (2015). Improvements to the Function Point Analysis Method: A Systematic Literature Review. IEEE Transactions on Engineering Management, 62(4), 495-506. https://doi.org/10.1109/TEM.2015.2453354.
2. Lavazza, L., Locoro, A., Liu, G., & Meli, R. (2023). Estimating software functional size via machine learning. ACM Transactions on Software Engineering and Methodology, 32(5), Article 114. https://doi.org/10.1145/3582575.
3. Hoc, H. T., Hai, V. V., & Nhung, H. L. T. K. (2021). An approach to adjust effort estimation of function point analysis. Software Engineering and Algorithms, 522-537. https://doi.org/10.1007/978-3-030-77442-4_45.
4. Sree, P. R., & Ramesh, S. N. S. V. S. C. (2016). Improving efficiency of fuzzy models for effort estimation by cascading & clustering techniques. Procedia Computer Science, 85, 278-285. https://doi.org/10.1016/j.procs.2016.05.234.
5. Nwaiwu, J. C., & Oluwadare, S. (2016). Analytic study of fuzzy-based model for software cost estimation. 2nd ACM International Conference on Computing Research and Innovations (CoRI). https://www.researchgate.net/publication/313351062_Analytic_Study_of_Fuzzy-based_model_for_Software_Cost_Estimation.
6. Setyohadi, D. B. (2019). Estimation of software development project success using fuzzy logics. Advances in Science, Technology and Engineering Systems Journal, 4(2), 280-287. https://doi.org/10.25046/aj040236
7. Xia, W., Ho, D., Capretz, L. F., & Ahmed, F. (2019). Updating weight values for function point counting. International Journal of Hybrid Intelligent Systems, 6(1), 1-14. https://doi.org/10.48550/arXiv.2005.11218.
8. Kaushik, A., Soni, A., & Soni, R. (2013). A type-2 fuzzy logic based framework for function points. International Journal of Intelligent Systems and Applications, 5(3), 74-82. doi.org/10.5815/ijisa.2013.03.08.
9. Bou Nassif, A., Azzeh, M., Idri, A., & Abran, A. (2019). Software development effort estimation using regression fuzzy models. Computational Intelligence and Neuroscience Journal. doi.org/10.48550/arXiv.1902.03608.
10. Mohamed, M., Emam, O., & Azzam, S. M. (2024). Software cost estimation prediction using a convolutional neural network and particle swarm optimization algorithm. Scientific Reports, 14, Article 13129. https://doi.org/10.1038/s41598-024-63025-8.
11. Malik, G., Cevik, M., Bera, S., & Yildirim, S. (2022). Software requirement specific entity extraction using transformer models. The 35th Canadian Conference on Artificial Intelligence. doi.org/10.21428/594757db.9e433d7c.
12. Bilous, D.A., & Kozlovskyi, A.V. (2024). Using function point analysis for professional service and maintenance IT projects: A tailoring approach for enhanced size and effort estimation. XI International Scientific and Practical Conference «Modern Science: Theoretical and Practical View», April 16-17, 2024, Madrid, Spain, 82-90 [in Ukrainian]. https://doi.org/10.5281/zenodo.11065612.
13. Bilous, D., & Kozlovskyi, A. (2025). Improving the efficiency of assessing the functional complexity of software development and support using fuzzy logic models. Youth in Science: Research, Problems, Prospects (МН-2025). https://conferences.vntu.edu.ua/index.php/mn/mn2025/paper/view/22288 [in Ukrainian]
14. Bilous, D., & Kozlovskyi, A. (2025). A frame knowledge model for fuzzy analysis of functional points in modern development using SaaS technologies. LIV All-Ukrainian Scientific and Technical Conference of the Faculty of Intellectual Information Technologies and Automation. https://conferences.vntu.edu.ua/ index.php/all-fksa/all-fksa-2025/paper/view/23168 [in Ukrainian].
15. Yukhymchuk, S.V. (2003). Mathematical risk models for decision support systems. Vinnytsia: Universum-Vinnytsia [in Ukrainian].
Citations
1. de Freitas Junior M., Fantinato M., Sun, V. Improvements to the Function Point Analysis Method: A Systematic Literature Review. IEEETransactions on Engineering Management. 2015. Vol. 62, №4, P. 495-506. URL: https://doi.org/10.1109/TEM.2015.2453354 (дата звернення: 22.02.2025).
2. Lavazza L., Locoro A., Liu G. Estimating software functional size via machine learning. ACM Transactions on Software Engineering and Methodology. 2023. Vol. 32, №5. URL: https://doi.org/10.1145/3582575 (дата звернення: 22.02.2025).
3. Hoc H. T., Hai V. V., Nhung H. L. T. K. An approach to adjust effort estimation of function point analysis. Software Engineering and Algorithms. 2021. P. 522-537. URL: https://doi.org/10.1007/978-3-030-77442-4_45 (дата звернення: 22.02.2025).
4. Sree P. R., Ramesh S. N. S. V. S. C. Improving efficiency of fuzzy models for effort estimation by cascading & clustering techniques. Procedia Computer Science. 2016. Vol. 85, P. 278-285. URL: https://doi.org/10.1016/j.procs.2016.05.234 (дата звернення: 22.02.2025).
5. Nwaiwu J. C., Oluwadare S. Analytic study of fuzzy-based model for software cost estimation. 2nd ACM International Conference on Computing Research and Innovations (CoRI). 2016. Vol. 1755. URL: https://www.researchgate.net/publication/313351062_Analytic_Study_of_Fuzzy-based_model_for_Software_Cost_Estimation (дата звернення: 22.02.2025)
6. Setyohadi D. B. Estimation of software development project success using fuzzy logics. Advances in Science, Technology and Engineering Systems Journal. 2019. Vol. 4, №2, P. 280-287. URL: https://doi.org/10.25046/aj040236 (дата звернення: 22.02.2025).
7. Xia W., Ho D., Capretz L. F. Updating weight values for function point counting. International Journal of Hybrid Intelligent Systems. 2019, Vol. 6, №1, P. 1-14. URL: https://doi.org/10.48550/arXiv.2005.11218 (дата звернення: 22.02.2025).
8. Kaushik A., Soni A., Soni R. A type-2 fuzzy logic based framework for function points. International Journal of Intelligent Systems and Applications, 2013, Vol. 5, №3, P. 74-82. URL: https://doi.org/10.5815/ijisa.2013.03.08 (дата звернення: 22.02.2025).
9. Bou Nassif A., Azzeh M., Idri A. Software development effort estimation using regression fuzzy models. Computational Intelligence and Neuroscience Journal. 2019. URL: https://doi.org/10.48550/arXiv.1902.03608 (дата звернення: 22.02.2025).
10. Mohamed M., Emam O., Azzam S. M. Software cost estimation prediction using a convolutional neural network and particle swarm optimization algorithm. Scientific Reports. 2024, Vol.14, URL: https://doi.org/10.1038/s41598-024-63025-8 (Дата звернення: 22.02.2025).
11. Malik G., Cevik M., Bera, S. Software requirement specific entity extraction using transformer models. The 35th Canadian Conference on Artificial Intelligence. 2024. URL: https://doi.org/10.21428/594757db.9e433d7c (дата звернення: 22.02.2025)
12. Білоус Д.А., Козловський, А.В. Використання аналізу функціональних точок для ІТ-проєктів підтримки та професійного сервісу: модифікований підхід для покращення оцінки розміру та витрат. XI Міжнародна науково-практична конференція «Сучасна наука: теоретичний та практичний погляд», 16-17 квітня 2024, Мадрид, Іспанія, C.82-90. URL: https://doi.org/10.5281/zenodo.11065612 (дата звернення: 22.02.2025).
13. Білоус Д.А., Козловський, А.В. Підвищення ефективності оцінки функціональної складності розробки та підтримки програмного забезпечення з використанням моделей нечіткої логіки. Молодь в науці: дослідження, проблеми, перспективи (МН-2025). 2025. URL: https://conferences.vntu.edu.ua/index.php/mn/mn2025/paper/view/22288 (дата звернення: 22.02.2025).
14. Білоус Д.А., Козловський А.В. Фреймова модель знань для нечіткого аналізу функціональних точок у сучасній розробці з використанням SaaS технологій. LIV Всеукраїнська науково-технічна конференція факультету інтелектуальних інформаційних технологій та автоматизації. 2025. URL: https://conferences.vntu.edu.ua/index.php/all-fksa/all-fksa-2025/paper/view/23168 (дата звернення: 22.02.2025).
15. Юхимчук С. В. Математичні моделі ризику для систем підтримки прийняття рішень: монографія. Вінниця: УНІВЕРСУМ-Вінниця. 2003. 188 с.