A DETERMINISTIC ONTOLOGY-DRIVEN FRAMEWORK FOR GOVERNANCE-AWARE AUTOMATED SOFTWARE REQUIREMENTS SPECIFICATION GENERATION

Abstract

Automated generation of Software Requirements Specifications (SRS) remains a persistent challenge in requirements engineering, particularly in governance-intensive environments where specifications must simultaneously preserve structural completeness, integrate domain-specific governance constraints, maintain explicit traceability, and support reproducible generation behaviour. Existing ontology-based, template-based, and AI-based approaches address these requirements only in isolation. This paper proposes and evaluates a deterministic ontology-driven transformation framework that generates SRS artifacts from an OntoUML-derived conceptual model (M), a governance-oriented domain ontology (D), and a declarative governance rule set (R), formalised as S = F(M, D, R). The framework employs a six-layer transformation architecture combining exhaustive baseline structural mapping with governance-aware semantic enrichment through declarative rule-based activation over a selectively merged ontology. A Python prototype using OWLReady2 was implemented and evaluated across five Service Request Management datasets and five FIBO-derived Financial Compliance datasets, using four metrics: Structural Coverage Ratio (SCR), Governance Enrichment Ratio (GER), Traceability Coverage (TC), and Deterministic Reproducibility (DR). Results confirm SCR = 1.00 and TC = 1.00 across all ten datasets, GER progressing from 0.0000 in baseline configurations to 0.1250 and 0.1429 in the two domains respectively, and DR confirmed through ten independent executions per dataset. These findings demonstrate that ontology-driven deterministic transformation provides a viable, extensible, and governance-aware foundation for automated SRS generation, addressing limitations present in existing template-based, ontology-based, and AI-based approaches.

Keywords: software requirements specification, ontology-driven requirements engineering, deterministic transformation, governance-aware generation, rule-based activation, FIBO

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