Modern software systems increasingly operate under dynamic requirements, heterogeneous dependencies, evolving architectures, and continuous performance constraints. These conditions challenge conventional software engineering approaches that treat program structure, dependencies, optimization, and adaptation as relatively stable processes. This paper proposes GraphEvoSE, an evolutionary graph intelligence framework for self-adaptive software development and optimization. The framework represents software artifacts, dependencies, execution relationships, quality attributes, and environmental constraints as a dynamic attributed graph and employs evolutionary search to identify and prioritize adaptive transformations. Its theoretical foundation is derived through cross-domain synthesis of the supplied literature, particularly studies demonstrating how structural relationships, interfaces, transport phenomena, and evolving material states influence system behavior. The framework incorporates graph construction, state monitoring, dependency-aware representation, evolutionary candidate generation, multi-objective evaluation, and feedback-driven adaptation. The conceptual analysis indicates that graph-based representation can provide greater structural awareness than isolated component-level optimization, while evolutionary search can support exploration of competing adaptation alternatives. The study further positions self-adaptation as a closed-loop process in which software architecture is continuously evaluated against functional and non-functional objectives. The proposed framework extends the evolutionary graph-reasoning perspective presented by Ramamurthy, Konduru, and Amanmadov (2026) toward a broader software development and optimization setting. The resulting model provides a research foundation for adaptive refactoring, dependency optimization, architectural evolution, and intelligent software maintenance, while recognizing limitations associated with graph-construction cost, search complexity, dynamic observability, and the absence of direct empirical validation in the present conceptual study.

Evolutionary Software Engineering, Graph Intelligence, Self-Adaptive Systems, Software Optimization

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