Context-Aware Generative AI for Automated Telecom Test Script Generation

• URL: http://arxiv.org/abs/2606.21151v1
• Date: Fri, 19 Jun 2026 06:44:31 GMT
• Title: Context-Aware Generative AI for Automated Telecom Test Script Generation
• Abstract summary: Existing solutions generate static test suites against a snapshot of the system.<n>As code, configurations, topologies, and key performance indicators (KPIs) evolve, these tests quickly become outdated or misaligned with the live system.<n>This paper presents a context-aware generative AI framework for automated telecom test script generation.
• Score: 0.0024792470319550896
• License: http://creativecommons.org/licenses/by/4.0/
• 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system; as code, configurations, topologies, and key performance indicators (KPIs) evolve, these tests quickly become outdated or misaligned with the live system.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.

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