DEFINITION AND FORMALIZATION OF THE SOFTWARE FUNCTIONAL STATE CONCEPT THROUGHOUT THE DEVELOPMENT LIFE CYCLE

Mariia Lyashkevych, Vasyl Lyashkevych, Roman Shuvar

Abstract


Background. Today, software is a critically important component of any information system. Its development requires significant resources and complex technical solutions, and the development of technologies is so rapid that not all concepts and definitions in the field of software are clearly formalized. This is especially true for the software functional state (SFS) throughout the software development life cycle (SDLC), as predicting all possible states is virtually impossible due to the dynamic nature of environments, changing requirements, component interactions, and the behavior of project participants. This creates a challenge for formalizing, analyzing, forecasting, monitoring, and managing these states.

Materials and Methods. The definition and formalization of SFSs encompass concepts from state theory in computer science, as well as quality models from international standards ISO/IEC 25010:2011 and the State Standard of Ukraine ISO/IEC 9126-1:2005. The defined concepts of SFS and SFS during SDLC are formalized mathematically, which allows building dynamic models of state evolution during SDLC based on the stochastic transition function. To build models, attributes such as functional compliance, reliability, vulnerability, testability, and others have been developed in combination with event-driven, finite-state machine, and state-driven models. Also presented are different types of SFS and their relationship with SDLC.

Results and Discussion. The research results include the formalization of SFS, the development of evaluation metrics, and practical recommendations for SFS analytics at all stages of SDLC, which enable proactive control of the quality, reliability, security, and compliance of software systems.

Conclusion. The formalization of the concept of SFSs, including their types, properties, and parameters, allowed for a reasonable connection to the SDLC phases. The proposed metrics and recommendations contribute to the development of SFS analytics, ensuring both the theoretical integrity of the approach and its practical applicability in the tasks of monitoring, analysis and predicting SFS. This methodology creates a new foundation for self-learning SDLC-oriented ecosystems in which SFSs are predicted, assessed and managed automatically in real-time.

Keywords: software development life cycle, software functional state, functional suitability, software state prediction, software functional state analytics, software state characteristics.


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References


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DOI: http://dx.doi.org/10.30970/eli.32.11

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