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

S. Anand, E. K. Burke, T. Y. Chen, J. Clark, M. B. Cohen, W. Grieskamp, M. Harman, M. J. Harrold, P. McMinn, A. Bertolino, et al

2013

Earlier work this paper cites.

M. Harman, Y. Jia, and Y. Zhang, “Achievements, open problems and challenges for search based software testing,” in 2015 IEEE 8th International Conference on Software Testing, Verification and Validation (ICST)

2015

Earlier work this paper cites.

J. Yoon, D. Jarrett, and M. van der Schaar, “Time-series generative adversarial networks,” in Advances in Neural Information Processing Systems

2019

Earlier work this paper cites.

A. Salman, “Test case generation from specifications using natural language processing,” 2020

2020

Earlier work this paper cites.

J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in neural information processing systems

2020

Earlier work this paper cites.

L. Perez, L. Ottens, and S. Viswanathan, “Automatic code generation using pre-trained language models,” arXiv preprint arXiv:2102.10535

Original

2021

Earlier work this paper cites.

J. Austin, A. Odena, M. I. Nye, M. Bosma, H. Michalewski, D. Dohan, E. Jiang, C. J. Cai, M. Terry, Q. V. Le, and C. Sutton, “Program synthesis with large language models,” CoRR

Original

2021

Earlier work this paper cites.

J. M. L. Alcaraz and N. Strodthoff, “Diffusion-based time series imputation and forecasting with structured state space models,” ArXiv

Original

2022

Earlier work this paper cites.

X. Hu, Q. Chen, H. Wang, X. Xia, D. Lo, and T. Zimmermann, “Correlating automated and human evaluation of code documentation generation quality,” ACM Transactions on Software Engineering and Methodology (TOSEM)

2022

Earlier work this paper cites.

M. Polese, L. Bonati, S. D’oro, S. Basagni, and T. Melodia, “Understanding o-ran: Architecture, interfaces, algorithms, security, and research challenges,” IEEE Communications Surveys & Tutorials

2023

Earlier work this paper cites.

R. Bourgerie and T. Zanouda, “Fault detection in telecom networks using bi-level federated graph neural networks,” in 2023 IEEE International Conference on Data Mining Workshops (ICDMW)

2023

Cited alongside, same era.

B. Tang, V. K. Shah, V. Marojevic, and J. H. Reed, “Ai testing framework for next-g o-ran networks: Requirements, design, and research opportunities,” IEEE Wireless Communications

2023

Cited alongside, same era.

A. Abo-eleneen, A. Palliyali, and C. Catal, “The role of reinforcement learning in software testing,” Information and Software Technology

2023

Cited alongside, same era.

A. Fan, B. Gokkaya, M. Harman, M. Lyubarskiy, S. Sengupta, S. Yoo, and J. M. Zhang, “Large language models for software engineering: Survey and open problems,” arXiv preprint arXiv:2310.03533

Original

2023

Cited alongside, same era.

X. Hou, Y. Zhao, Y. Liu, Z. Yang, K. Wang, L. Li, X. Luo, D. Lo, J. Grundy, and H. Wang, “Large language models for software engineering: A systematic literature review,” arXiv preprint arXiv:2308.10620

L. Lin, Z. Li, R. Li, X. Li, and J. Gao, “Diffusion models for time series applications: A survey,” arXiv preprint arXiv:2305.00624

Original

2023

Later among the works it cites.

M. Evtikhiev, E. Bogomolov, Y. Sokolov, and T. Bryksin, “Out of the bleu: How should we assess quality of the code generation models?,” Journal of Systems and Software

2023

Later among the works it cites.

A. Q. Jiang, A. Sablayrolles, A. Mensch, C. Bamford, D. S. Chaplot, D. d. l. Casas, F. Bressand, G. Lengyel, G. Lample, L. Saulnier, et al

Original

2023

Later among the works it cites.

B. Roziere, J. Gehring, F. Gloeckle, S. Sootla, I. Gat, X. E. Tan, Y. Adi, J. Liu, T. Remez, J. Rapin, et al

Original

2023

Later among the works it cites.

Y. Wei, Z. Wang, J. Liu, Y. Ding, and L. Zhang, “Magicoder: Source code is all you need,” arXiv preprint arXiv:2312.02120