In recent years, the evolution of Telecom towards achieving intelligent, autonomous, and open networks has led to an increasingly complex Telecom Software system, supporting various heterogeneous deployment scenarios, with multi-standard and multi-vendor support. As a result, it becomes a challenge for large-scale Telecom software companies to develop and test software for all deployment scenarios. To address these challenges, we propose a framework for Automated Test Generation for large-scale Telecom Software systems. We begin by generating Test Case Input data for test scenarios observed using a time-series Generative model trained on historical Telecom Network data during field trials. Additionally, the time-series Generative model helps in preserving the privacy of Telecom data. The generated time-series software performance data are then utilized with test descriptions written in natural language to generate Test Script using the Generative Large Language Model. Our comprehensive experiments on public datasets and Telecom datasets obtained from operational Telecom Networks demonstrate that the framework can effectively generate comprehensive test case data input and useful test code.

翻译：近年来，电信网络向智能、自治、开放方向演进，导致支持多种异构部署场景、多标准与多供应商的电信软件系统日益复杂。因此，大规模电信软件企业面临为所有部署场景开发和测试软件的挑战。为应对这些问题，我们提出了一种面向大规模电信软件系统的自动化测试生成框架。首先，利用基于历史现场试验电信网络数据训练的时间序列生成模型，生成测试场景的测试用例输入数据；该时间序列生成模型还能有效保护电信数据的隐私性。随后，将生成的时间序列软件性能数据与自然语言编写的测试描述相结合，通过生成式大语言模型生成测试脚本。我们在公开数据集及实际电信网络运营数据上的综合实验表明，该框架能够高效生成全面的测试用例数据输入及可用的测试代码。