# Test Code Generation for Telecom Software Systems using Two-Stage Generative Model

**Authors:** Mohamad Nabeel, Doumitrou Daniil Nimara, Tahar Zanouda  
**arXiv:** [2404.09249](https://arxiv.org/abs/2404.09249)  
**Date:** 2024-04-14  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2404.09249v1/

## Key points

- Evolution of Telecom towards intelligent, autonomous, and open networks
- Complex Telecom Software systems supporting various deployment scenarios with multi-standard and multi-vendor support
- Challenges faced by large-scale Telecom software companies in developing and testing software for different scenarios
- Proposed framework for Automated Test Generation for large-scale Telecom Software systems
- Main goal of the framework: provide an end-to-end automated testing pipeline for large-scale Telecom Software products
- Three stages of the method overview: Synthetic Test Input Data Generation, Test Case Script Generation, Utilization of Hybrid Generative Models
- Use of generative models like Diffusion Models with Structured State Space Sequence (S4) and timeGAN to generate meaningful samples for test cases
- Leveraging Large Language Models (LLM) trained on Code to generate meaningful test scripts based on task descriptions using synthetic data from generative models
- Utilization of hybrid generative models to automate tasks such as data input generation, test scenario generation, optimization, and code generation in an end-to-end flow
- Addressing a research gap by studying the end-to-end flow of automating Software Testing tasks using machine learning techniques and hybrid generative models

## Summary

The evolution of Telecom has shifted towards intelligent, autonomous, and open networks in recent years. This has resulted in complex Telecom Software systems that support various deployment scenarios with multi-standard and multi-vendor support. However, large-scale Telecom software companies face challenges in developing and testing software for all these scenarios. To address these challenges, a framework for Automated Test Generation for large-scale Telecom Software systems is proposed. The main goal of this framework is to provide an end-to-end automated testing pipeline for large-scale Telecom Software products. This pipeline aims to generate Test Case Input data for test scenarios observed during field trials and generate Test Scripts based on natural language test descriptions. The method overview includes three stages: Synthetic Test Input Data Generation, Test Case Script Generation, and Utilization of Hybrid Generative Models. A generative model trained on historical Telecom Network data from field trials is used to generate meaningful samples for test cases. Time-series generative models like Diffusion Models with Structured State Space Sequence (S4) and timeGAN are utilized for this purpose. Next, synthetic data generated from the generative model is used as examples within a prompt for a Large Language Model (LLM) trained on Code to generate meaningful test scripts based on the task description. Finally, the framework leverages hybrid generative models to automate tasks such as data input generation, test scenario generation, optimization, and code generation in an end-to-end flow. This paper addresses a gap in research by studying the end-to-end flow of automating Software Testing tasks using machine learning techniques and hybrid generative models. The study utilizes performance data collected from operational network sites comprising internal counters and time series data points monitoring SW Network performance from representative RAN nodes. Overall, the proposed framework demonstrates effectiveness in generating comprehensive test case input data and useful test scripts for large-scale Telecom Software systems through the utilization of advanced generative models and machine learning techniques.