Knowledge Representation Model for the Complexity Analysis of the Software Development using Saas/Paas Platforms

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.