CV

Education

Ph.D in Version Control Theory, GitHub University, 2018 (expected)
M.S. in Jekyll, GitHub University, 2014
B.S. in GitHub, GitHub University, 2012

Work experience

Spring 2024: Academic Pages Collaborator
- GitHub University
- Duties includes: Updates and improvements to template
- Supervisor: The Users
Fall 2015: Research Assistant
- GitHub University
- Duties included: Merging pull requests
- Supervisor: Professor Hub
Summer 2015: Research Assistant
- GitHub University
- Duties included: Tagging issues
- Supervisor: Professor Git

Skills

Skill 1
Skill 2
- Sub-skill 2.1
- Sub-skill 2.2
- Sub-skill 2.3
Skill 3

Publications

LLM-Based Recommender Systems for Violation Resolutions in Continuous Architectural Conformance

Prompt engineering and its implications on the energy consumption of large language models

R. Rubei, A. Moussaid, C. D. Sipio, and D. D. Ruscio, Prompt engineering and its implications on the energy consumption of large language models, 2025. doi: 10.48550/ARXIV.2501.05899. arXiv: 2501.05899.

On the energy consumption of atl transformations

R. Rubei, J. d. Rocco, and D. d. Ruscio, “On the energy consumption of atl transformations,” Software: Practice and Experience, vol. 55, no. 7, pp. 1145–1164, 2025. doi: https://doi.org/10.1002/spe.3410.

On the use of large language models in model-driven engineering

J. D. Rocco, D. D. Ruscio, C. D. Sipio, P. T. Nguyen, and R. Rubei, “On the use of large language models in model-driven engineering,” Softw. Syst. Model., vol. 24, no. 3, pp. 923–948, 2025. doi: 10.1007/S10270-025-01263-8.

Deepmig: A transformer-based approach to support coupled library and code migrations

J. D. Rocco, P. T. Nguyen, C. D. Sipio, R. Rubei, D. D. Ruscio, and M. D. Penta, “Deepmig: A transformer-based approach to support coupled library and code migrations,” Inf. Softw. Technol., vol. 177, p. 107 588, 2025. doi: 10.1016/J.INFSOF.2024.107588.

Leveraging synthetic trace generation of modeling operations for intelligent modeling assistants using large language models

V. Muttillo, C. Di Sipio, R. Rubei, and L. Berardinelli, “Leveraging synthetic trace generation of modeling operations for intelligent modeling assistants using large language models,” Information and Software Technology, vol. 186, p. 107 806, 2025, issn: 0950-5849. doi: https://doi.org/10.1016/j.infsof.2025.107806.

On the use of llms to support the development of domain-specific modeling languages

C. D. Sipio, R. Rubei, J. D. Rocco, D. D. Ruscio, and L. Iovino, On the use of llms to support the development of domain-specific modeling languages, M. Wimmer, A. Egyed, B. Combemale, and M. Chechik, Eds., 2024. doi: 10.1145/3652620.3687808.

Gptsniffer: A codebert-based classifier to detect source code written by chatgpt

P. T. Nguyen, J. D. Rocco, C. D. Sipio, R. Rubei, D. D. Ruscio, and M. D. Penta, “Gptsniffer: A codebert-based classifier to detect source code written by chatgpt,” J. Syst. Softw., vol. 214, p. 112 059, 2024. doi: 10.1016/J.JSS.2024.112059.

Modelxglue: A benchmarking framework for ml tools in mde

J. A. H. López, J. S. Cuadrado, R. Rubei, and D. Di Ruscio, “Modelxglue: A benchmarking framework for ml tools in mde,” Software and Systems Modeling, pp. 1–24, 2024.

Automated categorization of pre-trained models in software engineering: A case study with a hugging face dataset

C. D. Sipio, R. Rubei, J. D. Rocco, D. D. Ruscio, and P. T. Nguyen, “Automated categorization of pre-trained models in software engineering: A case study with a hugging face dataset,” in Proceedings of the 28th International Conference on Evaluation and Assessment in Software Engineering, EASE 2024, Salerno, Italy, June 18-21, 2024, ACM, 2024, pp. 351–356. doi: 10.1145/3661167.3661215.

Towards synthetic trace generation of modeling operations using in-context learning approach

V. Muttillo, C. D. Sipio, R. Rubei, L. Berardinelli, and M. Dehghani, “Towards synthetic trace generation of modeling operations using in-context learning approach,” in Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering, ASE 2024, Sacramento, CA, USA, October 27 - November 1, 2024, V. Filkov, B. Ray, and M. Zhou, Eds., ACM, 2024, pp. 619–630. doi: 10.1145/3691620.3695058.

An empirical study on code coverage of performance testing

M. Imran, V. Cortellessa, D. D. Ruscio, R. Rubei, and L. Traini, “An empirical study on code coverage of performance testing,” in Proceedings of the 28th International Conference on Evaluation and Assessment in Software Engineering, EASE 2024, Salerno, Italy, June 18-21, 2024, ACM, 2024, pp. 48–57. doi: 10.1145/3661167.3661196.

Playmydata: A curated dataset of multi-platform video games

A. D’Angelo, C. D. Sipio, C. Politowski, and R. Rubei, “Playmydata: A curated dataset of multi-platform video games,” in 21st IEEE/ACM International Conference on Mining Software Repositories, MSR 2024, Lisbon, Portugal, April 15-16, 2024, D. Spinellis, A. Bacchelli, and E. Constantinou, Eds., ACM, 2024, pp. 525–529. doi: 10.1145/3643991.3644869.

Towards automating model-based systems engineering in industry - an experience report

J. Cederbladh, L. Berardinelli, H. Bruneliere, et al., “Towards automating model-based systems engineering in industry - an experience report,” in IEEE International Systems Conference, SysCon 2024, Montreal, QC, Canada, April 15-18, 2024, IEEE, 2024, pp. 1–8. doi: 10.1109/SYSCON61195.2024.10553610.

Supporting early-safety analysis of iot systems by exploiting testing techniques

D. Clerissi, J. D. Rocco, D. D. Ruscio, et al., Supporting early-safety analysis of iot systems by exploiting testing techniques, 2023. doi: 10.1109/MODELS-C59198.2023.00089.

Hybridrec: A recommender system for tagging github repositories

J. D. Rocco, D. D. Ruscio, C. D. Sipio, P. T. Nguyen, and R. Rubei, “Hybridrec: A recommender system for tagging github repositories,” Appl. Intell., vol. 53, no. 8, pp. 9708–9730, 2023. doi: 10.1007/S10489-022-03864-Y.

Dealing with popularity bias in recommender systems for third-party libraries: How far are we?

P. T. Nguyen, R. Rubei, J. D. Rocco, C. D. Sipio, D. D. Ruscio, and M. D. Penta, “Dealing with popularity bias in recommender systems for third-party libraries: How far are we?” In 20th IEEE/ACM International Conference on Mining Software Repositories, MSR 2023, Melbourne, Australia, May 15-16, 2023, IEEE, 2023, pp. 12–24. doi: 10.1109/MSR59073.2023.00016.

Providing upgrade plans for third-party libraries: A recommender system using migration graphs

R. Rubei, D. D. Ruscio, C. D. Sipio, J. D. Rocco, and P. T. Nguyen, “Providing upgrade plans for third-party libraries: A recommender system using migration graphs,” Appl. Intell., vol. 52, no. 10, pp. 12 000–12 015, 2022. doi: 10.1007/S10489-021-02911-4.

Deeplib: Machine translation techniques to recommend upgrades for third-party libraries

P. T. Nguyen, J. D. Rocco, R. Rubei, C. D. Sipio, and D. D. Ruscio, “Deeplib: Machine translation techniques to recommend upgrades for third-party libraries,” Expert Syst. Appl., vol. 202, p. 117 267, 2022. doi: 10.1016/J.ESWA.2022.117267.

Endowing third-party libraries recommender systems with explicit user feedback mechanisms

R. Rubei, C. D. Sipio, J. D. Rocco, D. D. Ruscio, and P. T. Nguyen, “Endowing third-party libraries recommender systems with explicit user feedback mechanisms,” in IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2022, Honolulu, HI, USA, March 15-18, 2022, IEEE, 2022, pp. 817–821. doi: 10.1109/SANER53432.2022.00099.

Machine learning methods for model classification: A comparative study

J. A. H. López, R. Rubei, J. S. Cuadrado, and D. D. Ruscio, “Machine learning methods for model classification: A comparative study,” in Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems, MODELS 2022, Montreal, Quebec, Canada, October 23-28, 2022, E. Syriani, H. A. Sahraoui, N. Bencomo, and M. Wimmer, Eds., ACM, 2022, pp. 165–175. doi: 10.1145/3550355.3552461.

A lightweight approach for the automated classification and clustering of metamodels

R. Rubei, J. D. Rocco, D. D. Ruscio, P. T. Nguyen, and A. Pierantonio, A lightweight approach for the automated classification and clustering of metamodels, 2021. doi: 10.1109/MODELS-C53483.2021.00074.

A lightweight approach for the automated classification and clustering of metamodels

Development of recommendation systems for software engineering: The CROSSMINER experience

J. D. Rocco, D. D. Ruscio, C. D. Sipio, P. T. Nguyen, and R. Rubei, “Development of recommendation systems for software engineering: The CROSSMINER experience,” Empir. Softw. Eng., vol. 26, no. 4, p. 69, 2021. doi: 10.1007/S10664-021-09963-7.

Recommending third-party library updates with LSTM neural networks

P. T. Nguyen, J. D. Rocco, R. Rubei, and D. D. Ruscio, “Recommending third-party library updates with LSTM neural networks,” in Proceedings of the 11th Italian Information Retrieval Workshop 2021, Bari, Italy, September 13-15, 2021, V. W. Anelli, T. D. Noia, N. Ferro, and F. Narducci, Eds., ser. CEUR Workshop Proceedings, vol. 2947, CEUR-WS.org, 2021. url: https://ceur-ws.org/Vol-2947/paper7.pdf.

Postfinder: Mining stack overflow posts to support software developers

R. Rubei, C. D. Sipio, P. T. Nguyen, J. D. Rocco, and D. D. Ruscio, “Postfinder: Mining stack overflow posts to support software developers,” Inf. Softw. Technol., vol. 127, p. 106 367, 2020. doi: 10.1016/J.INFSOF.2020.106367.

An automated approach to assess the similarity of github repositories

P. T. Nguyen, J. D. Rocco, R. Rubei, and D. D. Ruscio, “An automated approach to assess the similarity of github repositories,” Softw. Qual. J., vol. 28, no. 2, pp. 595–631, 2020. doi: 10.1007/S11219-019-09483-0.

A multinomial naive bayesian (MNB) network to automatically recommend topics for github repositories

C. D. Sipio, R. Rubei, D. D. Ruscio, and P. T. Nguyen, “A multinomial naive bayesian (MNB) network to automatically recommend topics for github repositories,” in EASE ’20: Evaluation and Assessment in Software Engineering, Trondheim, Norway, April 15-17, 2020, J. Li, L. Jaccheri, T. Dingsøyr, and R. Chitchyan, Eds., ACM, 2020, pp. 71–80. doi: 10.1145/3383219.3383227.

Topfilter: An approach to recommend relevant github topics

J. D. Rocco, D. D. Ruscio, C. D. Sipio, P. T. Nguyen, and R. Rubei, “Topfilter: An approach to recommend relevant github topics,” in ESEM ’20: ACM / IEEE International Symposium on Empirical Software Engineering and Measurement, Bari, Italy, October 5-7, 2020, M. T. Baldassarre, F. Lanubile, M. Kalinowski, and F. Sarro, Eds., ACM, 2020, 21:1–21:11. doi: 10.1145/3382494.3410690.

CrossSim: exploiting mutual relationships to detect similar OSS projects

Nguyen, Phuong T., et al. "CrossSim: Exploiting mutual relationships to detect similar OSS projects." 2018 44th Euromicro conference on software engineering and advanced applications (SEAA). IEEE, 2018.

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