Test Code Generation for Telecom Software Systems using Two-Stage Generative Model
This addresses the problem of automated test generation for large-scale Telecom software companies, but it appears incremental as it combines existing generative models for a specific domain.
The paper tackles the challenge of testing complex Telecom software systems by proposing a two-stage generative model framework that generates test case input data and test scripts, demonstrating effectiveness on public and Telecom datasets.
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.