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

arXiv: https://arxiv.org/abs/2404.09249 · HTML: https://mlsanity.com/paper/2404.09249/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 34 references that are in the map.
- First posted on arXiv 14 Apr 2024.

## Builds on (most cited first, 21 of 21)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [Large Language Models for Software Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2308.10620/) (2023)
- [CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-X](https://mlsanity.com/paper/2303.17568/) (2023)
- [Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models](https://mlsanity.com/paper/2208.09399/) (2022)
- [Large Language Models for Software Engineering: Survey and Open Problems](https://mlsanity.com/paper/2310.03533/) (2023)
- [Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineering](https://mlsanity.com/paper/2401.08500/) (2024)
- [If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents](https://mlsanity.com/paper/2401.00812/) (2024)
- [Automated Unit Test Improvement using Large Language Models at Meta](https://mlsanity.com/paper/2402.09171/) (2024)
- [Diffusion Models for Time Series Applications: A Survey](https://mlsanity.com/paper/2305.00624/) (2023)
- [Towards an Understanding of Large Language Models in Software Engineering Tasks](https://mlsanity.com/paper/2308.11396/) (2023)
- [Out of the BLEU: how should we assess quality of the Code Generation models?](https://mlsanity.com/paper/2208.03133/) (2022)
- [Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code](https://mlsanity.com/paper/2311.07989/) (2023)
- [L2CEval: Evaluating Language-to-Code Generation Capabilities of Large Language Models](https://mlsanity.com/paper/2309.17446/) (2023)
- [The Program Testing Ability of Large Language Models for Code](https://mlsanity.com/paper/2310.05727/) (2023)
- [Turbulence: Systematically and Automatically Testing Instruction-Tuned Large Language Models for Code](https://mlsanity.com/paper/2312.14856/) (2023)
- [Automatic Code Generation using Pre-Trained Language Models](https://mlsanity.com/paper/2102.10535/) (2021)
- [Fault Detection in Telecom Networks using Bi-level Federated Graph Neural Networks](https://mlsanity.com/paper/2311.14469/) (2023)
- [AI Testing Framework for Next-G O-RAN Networks: Requirements, Design, and Research Opportunities](https://mlsanity.com/paper/2211.03979/) (2022)

## Built on by (most cited first, 2 of 2)

- [Telecom Foundation Models: Applications, Challenges, and Future Trends](https://mlsanity.com/paper/2408.03964/) (2024)
- [Chat3GPP: An Open-Source Retrieval-Augmented Generation Framework for 3GPP Documents](https://mlsanity.com/paper/2501.13954/) (2025)

## Abstract (arXiv)

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.