SEMANTIC API COMPATIBILITY ANALYSIS FOR LARGESCALE ENTERPRISE INTEGRATION SYSTEMS

Abstract

Large-scale enterprise integration systems increasingly depend on application programming interfaces (apis) to enable communication among cloud-native services, microservices, enterprise applications, and external partners. As enterprise api ecosystems expand, maintaining semantic compatibility across evolving apis has become essential for ensuring interoperability, reducing integration failures, and supporting continuous software evolution. Conventional compatibility validation techniques primarily focus on syntactic interface matching and often fail to identify semantic inconsistencies that affect business functionality. This paper proposes a semantic api compatibility analysis framework integrating openapi specification, raml, machine learning, semantic modeling, cloud-native devsecops, automated governance, and enterprise lifecycle management. The proposed methodology performs semantic validation of api contracts, identifies compatibility conflicts, predicts integration risks, and supports automated migration throughout enterprise software evolution. Experimental evaluation demonstrates improvements in interoperability, governance consistency, deployment reliability, integration quality, and operational scalability. The proposed framework provides a scalable and productionready solution for semantic api compatibility management in modern enterprise integration environments. Keywords— semantic api compatibility, openapi specification, enterprise integration, api governance, machine learning, devsecops, interoperability, microservices.