mlsanityData to 7 Oct 2026

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

arXiv 2404.09249 ↗First posted 14 Apr 2024cs.SE cs.CL cs.LGcited by 2

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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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 21 of its 34 references that are in the map. First posted on arXiv 14 Apr 2024.

Builds on

21 of 34 references.

References · 21

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2021Program Synthesis with Large Language Modelscited by 3,509
  3. 2023Mistral 7Bcited by 3,065
  4. 2023Code Llama: Open Foundation Models for Codecited by 2,105
  5. 2023Large Language Models for Software Engineering: A Systematic Literature Reviewcited by 389
  6. 2023CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-Xcited by 294
  7. 2022Diffusion-based Time Series Imputation and Forecasting with Structured State Space Modelscited by 195
  8. 2023Large Language Models for Software Engineering: Survey and Open Problemscited by 178
  9. 2024Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineeringcited by 100
  10. 2024If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agentscited by 77
  11. 2024Automated Unit Test Improvement using Large Language Models at Metacited by 73
  12. 2023Diffusion Models for Time Series Applications: A Surveycited by 72
9 more
  1. 2023Towards an Understanding of Large Language Models in Software Engineering Taskscited by 67
  2. 2022Out of the BLEU: how should we assess quality of the Code Generation models?cited by 65
  3. 2023Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Codecited by 62
  4. 2023L2CEval: Evaluating Language-to-Code Generation Capabilities of Large Language Modelscited by 22
  5. 2023The Program Testing Ability of Large Language Models for Codecited by 11
  6. 2023Turbulence: Systematically and Automatically Testing Instruction-Tuned Large Language Models for Codecited by 9
  7. 2021Automatic Code Generation using Pre-Trained Language Modelscited by 8
  8. 2023Fault Detection in Telecom Networks using Bi-level Federated Graph Neural Networkscited by 5
  9. 2022AI Testing Framework for Next-G O-RAN Networks: Requirements, Design, and Research Opportunitiescited by 4

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Citing papers per year: 2024: 1; 2025: 1.

2 papers.