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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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Published in 2018 44th Euromicro Conference on Software Engineering and Advanced Applications, 2018
Recommended citation: 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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Published in 2025 IEEE 22nd International Conference on Software Architecture Companion (ICSA-C), 2025
Published in Workshop Proceedings, 2025
This paper discusses prompt engineering and its implications on the energy consumption of large language models.
Recommended citation: 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.
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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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Published in MODELS Workshop Proceedings, 2023
This paper focuses on supporting early-safety analysis of IoT systems by exploiting testing techniques.
Recommended citation: 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.
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Published in MODELS Workshop Proceedings, 2021
This paper proposes a lightweight approach for the automated classification and clustering of metamodels.
Recommended citation: 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.
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Published in MODELS Workshop Proceedings, 2021
This paper proposes a lightweight approach for the automated classification and clustering of metamodels.
Recommended citation: 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.
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