Context-Aware Generative AI for Automated Telecom Test Script Generation

Researchers present a context-aware generative AI framework for automated telecom test script generation that continuously adapts to live system changes rather than relying on static test suites. The system uses a knowledge graph, delta-detection engine, and RAG-enhanced AI agent to automatically create, update, or retire test cases as code, configurations, and KPIs evolve, significantly reducing manual testing effort.

This research addresses a critical pain point in telecom software testing: the rapid obsolescence of static test suites. Traditional automated testing generates comprehensive test sets against a system snapshot, but as configurations and code evolve in production environments, these tests become misaligned with reality, forcing teams to regenerate entire suites—a labor-intensive and inefficient process.

The framework's innovation lies in treating testing as a continuous, adaptive process rather than a discrete artifact. By maintaining a live knowledge graph as a single source of truth and employing fine-grained delta detection, the system identifies exactly which tests require modification, avoiding unnecessary regeneration. The integration of generative AI via the Model Context Protocol enables autonomous test case creation while Retrieval-Augmented Generation enriches decision-making with historical and domain-specific knowledge.

For the telecom industry, this approach promises substantial operational efficiency gains. Testing cycles accelerate when only affected tests are updated; manual effort decreases as automation handles change detection and adaptation; and test relevance improves by staying synchronized with live systems. This directly impacts DevOps teams managing complex telecom infrastructures where rapid iteration and reliability are critical.

The framework's applicability across both software systems and network infrastructure suggests broader potential. As telecom networks become increasingly software-defined and cloud-native, continuous testing automation becomes essential. Organizations implementing such systems could reduce testing overhead by 30-50% while improving deployment velocity and system stability, creating competitive advantages in fast-moving markets.

• →Context-aware AI framework automatically detects and adapts only affected test cases rather than regenerating entire test suites.
• →Live knowledge graph serves as single source of truth, enabling fine-grained change detection via delta engine.
• →RAG integration enriches AI reasoning with domain knowledge and historical testing artifacts for telecom systems.
• →Framework reduces manual testing effort, accelerates test cycles, and improves alignment between tests and production systems.
• →Applicability demonstrated across software and network infrastructure suggests potential for widespread adoption in telecom DevOps.