Postdoctoral Researcher

Publications

You can also find my articles on my Google Scholar profile.

Journal Articles

Is code coverage of performance tests related to source code features? An empirical study on open-source Java systems

Published in Empirical Software Engineering, 2025

This paper propose an MDEframework named MASTER-LLM that combines different MDE tools and paradigms to support industrial and academic practitioners.

Recommended citation: Imran, Muhammad, et al. "Is code coverage of performance tests related to source code features? An empirical study on open-source Java systems." Empirical Software Engineering 30.6 (2025): 157.
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Leveraging synthetic trace generation of modeling operations for intelligent modeling assistants using large language models

Published in Information and Software Technology, 2025

This paper propose an MDEframework named MASTER-LLM that combines different MDE tools and paradigms to support industrial and academic practitioners.

Recommended citation: Muttillo, Vittoriano, et al. "Leveraging synthetic trace generation of modeling operations for intelligent modeling assistants using large language models." Information and Software Technology 186 (2025): 107806.
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On the energy consumption of atl transformations

Published in Software: Practice and Experience, 2025

This paper investigates the energy consumption of ATL transformations.

Recommended citation: 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.
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On the use of large language models in model-driven engineering

Published in Softw. Syst. Model., 2025

This paper discusses the use of large language models in model-driven engineering.

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

Published in Inf. Softw. Technol., 2025

This paper presents Deepmig, a transformer-based approach to support coupled library and code migrations.

Recommended citation: 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.
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Leveraging synthetic trace generation of modeling operations for intelligent modeling assistants using large language models

Published in Information and Software Technology, 2025

This paper explores leveraging synthetic trace generation of modeling operations for intelligent modeling assistants using large language models.

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

Published in J. Syst. Softw., 2024

This paper presents GPTSniffer, a CodeBERT-based classifier to detect source code written by ChatGPT.

Recommended citation: 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.
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Modelxglue: A benchmarking framework for ml tools in mde

Published in Software and Systems Modeling, 2024

This paper introduces ModelXGlue, a benchmarking framework for ML tools in MDE.

Recommended citation: 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.
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Hybridrec: A recommender system for tagging github repositories

Published in Appl. Intell., 2023

This paper introduces HybridRec, a recommender system for tagging GitHub repositories.

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

Published in Appl. Intell., 2022

This paper describes a recommender system using migration graphs to provide upgrade plans for third-party libraries.

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

Published in Expert Syst. Appl., 2022

This paper proposes Deeplib, using machine translation techniques to recommend upgrades for third-party libraries.

Recommended citation: 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.
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Development of recommendation systems for software engineering: The CROSSMINER experience

Published in Empir. Softw. Eng., 2021

This paper details the development of recommendation systems for software engineering, focusing on the CROSSMINER experience.

Recommended citation: 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.
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Postfinder: Mining stack overflow posts to support software developers

Published in Inf. Softw. Technol., 2020

This paper introduces PostFinder, a tool for mining Stack Overflow posts to support software developers.

Recommended citation: 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.
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An automated approach to assess the similarity of github repositories

Published in Softw. Qual. J., 2020

This paper presents an automated approach to assess the similarity of GitHub repositories.

Recommended citation: 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.
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Conference Papers

Coupling LLMs and Model-Driven Engineering to Support Synthetic Generation of BPMN Artifacts

Published in 2025 ACM/IEEE 28th International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C), 2025

The paper presents an MDE framework called BP-MASTER-LLM, which is devoted to supporting the specification of BPM models by combining a set of automated modeling tools, i.e., model-event recorder and modeling assistant.

Recommended citation: Muttillo, Vittoriano, et al. "Coupling LLMs and model-driven engineering to support synthetic generation of BPMN artifacts." 2025 ACM/IEEE 28th International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C). IEEE, 2025.
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Automated categorization of pre-trained models in software engineering: A case study with a hugging face dataset

Published in Proceedings of the 28th International Conference on Evaluation and Assessment in Software Engineering, EASE 2024, 2024

This paper presents an automated categorization of pre-trained models in software engineering using a Hugging Face dataset.

Recommended citation: 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.
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Towards synthetic trace generation of modeling operations using in-context learning approach

Published in Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering, ASE 2024, 2024

This paper explores synthetic trace generation of modeling operations using an in-context learning approach.

Recommended citation: 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.
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An empirical study on code coverage of performance testing

Published in Proceedings of the 28th International Conference on Evaluation and Assessment in Software Engineering, EASE 2024, 2024

This paper presents an empirical study on code coverage of performance testing.

Recommended citation: 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.
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Playmydata: A curated dataset of multi-platform video games

Published in 21st IEEE/ACM International Conference on Mining Software Repositories, MSR 2024, 2024

This paper introduces PlayMyData, a curated dataset of multi-platform video games.

Recommended citation: 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.
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Towards automating model-based systems engineering in industry - an experience report

Published in IEEE International Systems Conference, SysCon 2024, 2024

This paper discusses automating model-based systems engineering in industry, presenting an experience report.

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

Published in 20th IEEE/ACM International Conference on Mining Software Repositories, MSR 2023, 2023

This paper investigates popularity bias in recommender systems for third-party libraries.

Recommended citation: 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.
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Endowing third-party libraries recommender systems with explicit user feedback mechanisms

Published in IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2022, 2022

This paper explores explicit user feedback mechanisms for third-party libraries recommender systems.

Recommended citation: 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.
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Machine learning methods for model classification: A comparative study

Published in Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems, MODELS 2022, 2022

This paper provides a comparative study of machine learning methods for model classification.

Recommended citation: 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.
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Recommending third-party library updates with LSTM neural networks

Published in Proceedings of the 11th Italian Information Retrieval Workshop 2021, 2021

This paper explores recommending third-party library updates using LSTM neural networks.

Recommended citation: 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.
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A multinomial naive bayesian (MNB) network to automatically recommend topics for github repositories

Published in EASE ’20: Evaluation and Assessment in Software Engineering, 2020

This paper proposes a Multinomial Naive Bayesian (MNB) network to automatically recommend topics for GitHub repositories.

Recommended citation: 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.
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Topfilter: An approach to recommend relevant github topics

Published in ESEM ’20: ACM / IEEE International Symposium on Empirical Software Engineering and Measurement, 2020

This paper introduces TopFilter, an approach to recommend relevant GitHub topics.

Recommended citation: 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.
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Workshop Papers

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

Published in Workshop Proceedings, 2024

This paper discusses the use of LLMs to support the development of domain-specific modeling languages.

Recommended citation: 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.
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