Context-Aware Generative AI for Automated Telecom Test Script Generation

2026-06-19

AI Summary

This study addresses the limitations of existing telecom test script generation approaches, which rely on static system snapshots and struggle to adapt to dynamic changes in code, configurations, network topology, and KPIs, thereby compromising test effectiveness. To overcome this, the authors propose a continuously adaptive test evolution framework that leverages a real-time knowledge graph, a fine-grained change detection engine, and a context-aware generative AI agent to automatically create, update, and retire test cases. The work introduces an innovative delta-based test generation mechanism grounded in incremental knowledge graph updates, integrating Model Context Protocol (MCP) with Retrieval-Augmented Generation (RAG) to enable precise, context-aware, and continuous evolution of automated test scripts for telecom systems. Experimental results demonstrate that the approach significantly enhances test relevance, reduces manual intervention, and accelerates the testing cycle.

Abstract

Automated test generation for telecom software systems and networks has advanced significantly with the adoption of machine learning and rule-based approaches. However, most existing solutions generate static test suites against a snapshot of the system; as code, configurations, topologies, and key performance indicators (KPIs) evolve, these tests quickly become outdated or misaligned with the live system. There is currently no widely adopted solution that continuously detects fine-grained changes and selectively adapts only the affected tests without regenerating entire test suites. This paper presents a context-aware generative AI framework for automated telecom test script generation that treats testing as a continuously adapting process driven by the current state of the system rather than a static artifact. The central contribution is delta-conditioned test generation over a live knowledge graph: our approach employs a continuously updated knowledge graph (KG) as a single source of truth, a delta engine for fine-grained change detection, and a KG-guided generative AI agent, operating via the Model Context Protocol (MCP), to create, update, or retire test cases automatically. We further integrate Retrieval-Augmented Generation (RAG) to enrich reasoning with telecom-domain knowledge and historical artifacts. We demonstrate applicability across software-system and telecom-network use cases, including a Python-based KPI monitoring application managed in GitLab, and show how the framework reduces manual effort, improves test relevance, and accelerates test cycles.

Problem

• automated test generation
• telecom software systems
• context-aware AI
• test suite maintenance
• system evolution

Innovation

• context-aware generative AI
• knowledge graph
• delta-conditioned test generation
• Retrieval-Augmented Generation (RAG)
• Model Context Protocol (MCP)