List of publication

Journal publication

• Di Rocco, J., Di Ruscio, D., Di Sipio, C., Nguyen, P.T., and Rubei, R., Development of recommendation systems for software engineering: the CROSSMINER experience. Empirical Software Engineering, 26(4):1–40, 2021. DOI: https://doi.org/10.1007/s10664-021-09963-7.
• Nguyen, P.T., Di Rocco, J., Di Sipio, C., Di Ruscio, D., and Di Penta, M., Recommend- ing api function calls and code snippets to support software development. IEEE Trans- actions on Software Engineering, pages 1–1, 2021, DOI: 10.1109/TSE.2021.3059907.
• Duong, L. T., Nguyen, P. T., Di Sipio, C., and Di Ruscio, D., Automated fruit recognition using EfficientNet and MixNet, Computers and Electronics in Agriculture,Volume 171, 2020, 105326,ISSN 0168-1699, DOI: https://doi.org/10.1016/j.compag.2020.105326.
• Rubei, R., Di Sipio, C., Nguyen, P.T., Di Rocco, J., and Di Ruscio, D., PostFinder: Mining Stack Overflow posts to support software developers, Information and Software Technology, Volume 127, 2020,106367, ISSN 0950-5849, DOI: https://doi.org/10. 1016/j.infsof.2020.106367.
• Di Rocco, J., Di Ruscio, D., Di Sipio, C. Nguyen, P.T., and Pierantonio, A., MemoRec: a recommender system for assisting modelers in specifying metamodels. Softw Syst Model (2022). DOI: https://doi.org/10.1007/s10270-022-00994-2.
• Nguyen, P.T., Di Rocco, J., Rubei, R., Di Sipio C., and Di Ruscio, D., DeepLib: Machine translation techniques to recommend upgrades for third-party libraries, Expert Systems with Applications, Volume 202, 2022, 117267, ISSN 0957-4174, DOI: https://doi.org/10.1016/j.eswa.2022.117267.
• Di Rocco, J., Di Ruscio, D., Di Sipio, C., Nguyen, P.T., and Rubei, R., HybridRec: A recommender system for tagging GitHub repositories. Applied Intelligence (2022). DOI: https://doi.org/10.1007/s10489-022-03864-y.
• Rubei, R., Di Ruscio, D., Di Sipio, C, Di Rocco J., and Nguyen, P.T., Providing upgrade plans for third-party libraries: a recommender system using migration graphs. Appl.Intell 52, 12000–12015 (2022). DOI: https://doi.org/10.1007/s10489-021-02911-4.
• Nguyen, P. T., Di Sipio, C., Di Rocco, J., Di Penta, M., and Di Ruscio, D., Fitting Missing API Puzzles with Machine Translation Techniques, Journal of Expert Systems With Applications, 2022, DOI: https://doi.org/10.1016/j.eswa.2022.119477

Conference publication

• Di Rocco, J., Di Ruscio, D., Di Sipio, C., Nguyen, P.T., and Rubei, R.. Topfilter: An approach to recommend relevant github topics. In Proceedings of the 14th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM), ESEM ’20, New York, NY, USA, 2020. Association for Computing Machinery. ISBN 9781450375801. DOI: https://doi.org/10.1145/3382494.3410690
• Di Sipio, C., Rubei, R., Di Ruscio, D., and Nguyen, P.T., A multinomial naïve bayesian (mnb) network to automatically recommend topics for github repositories. In Pro- ceedings of the Evaluation and Assessment in Software Engineering, EASE ’20, page 71–80, New York, NY, USA, 2020. Association for Computing Machinery. ISBN 9781450377317. doi: 10.1145/3383219.3383227. DOI: https://doi.org/10.1145/ 3383219.3383227.
• Nguyen, P.T., Di Ruscio, D., Di Rocco, J., Di Sipio, C., and Di Penta, M., Adversarial machine learning: On the resilience of third-party library recommender systems. In Evaluation and Assessment in Software Engineering, EASE 2021, page 247–253, New York, NY, USA, 2021. Association for Computing Machinery. ISBN 9781450390538. DOI: https://doi.org/10.1145/3463274.3463809.
• Di Rocco, J., Di Sipio, C., Di Ruscio, D., and Nguyen, P.T., A GNN-based Recom- mender System to Assist the Specification of Metamodels and Models, 2021 ACM/IEEE 24th International Conference on Model Driven Engineering Languages and Systems (MODELS), 2021, pp. 70-81, DOI: 10.1109/MODELS50736.2021.00016
• Rubei, R., Di Sipio, C., Di Rocco, J., Di Ruscio, D., and Nguyen, P.T., Endowing third-party libraries recommender systems with explicit user feedback mechanisms, 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), 2022, pp. 817-821, DOI: 10.1109/SANER53432.2022.00099
• Di Sipio, C., Di Rocco, J., Di Ruscio, D. and, Nguyen, P.T., 2021. A Low-Code Tool Supporting the Development of Recommender Systems. In Fifteenth ACM Conference on Recommender Systems (RecSys ’21). Association for Computing Machinery, New York, NY, USA, 741–744. DOI: https://doi.org/10.1145/3460231.3478885
• Nguyen, P. T., Di Sipio, C, Di Rocco, J., Di Penta, M., and Di Ruscio, D., Adversarial Attacks to API Recommender Systems: Time to Wake Up and Smell the Coffee?, 2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE), 2021, pp. 253-265, DOI: 10.1109/ASE51524.2021.9678946
• Di Sipio C. 2022. Automating the design of recommender systems: from foundational aspects to actual development. In Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings (MODELS ’22).DOI:
• Di Rocco J., Di Sipio, C, Nguyen, P.T., Di Ruscio, D, and Pierantonio, A. 2022. Finding with NEMO: a recommender system to forecast the next modeling operations. In Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems (MODELS ’22). DOI:
• Di Sipio, C., Di Rocco, J., Di Ruscio, D. et al. MORGAN: a modeling recommender system based on graph kernel. Softw Syst Model 22, 1427–1449 (2023). https://doi.org/10.1007/s10270-023-01102-8
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Workshop publications

• Di Rocco, J. and Di Sipio, C. ResyDuo: Combining data models and CF-based recommender systems to develop Arduino projects. The 5th International Workshop on Multi-Paradigm Modeling for Cyber-Physical Systems, 2023
• Clerissi, D., Di Rocco, J., Di Ruscio, D., Di Sipio, C., Ihirwe, F., Mariani, L., Micucci D.,, Rossi, M.T., Rubei, R., Supporting Early-Safety Analysis of IoT Systems by Exploiting Testing Techniques. The 5th International Workshop on Multi-Paradigm Modeling for Cyber-Physical Systems, 2023
• Di Sipio, C., Di Ruscio, D., and Nguyen, P.T. Democratizing the development of recommender systems by means of low-code platforms, In Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings, MODELS ’20, New York, NY, USA, 2020. Association for Computing Machinery. ISBN 9781450381352. DOI: https://doi.org/10. 1145/3417990.3420202
• Di Rocco, J., Di Ruscio, D., Di Sipio, C., Nguyen, P. T., and Pomo, C. (2021). On the need for a body of knowledge on recommender systems. In Proceedings of the Joint KaRS and ComplexRec Workshop. URL: https://ceur-ws.org/Vol-2960/paper5.pdf.
• Rubei, R., and Di Sipio, C. (2021). AURYGA: A Recommender System for Game Tagging. The 11th Italian Information Retrieval Workshop, 2021. URL: https://ceur-ws. org/Vol-2947/paper10.pdf.
• Nguyen, P. T., Di Rocco, J., Rubei, R., Di Sipio, C., and Di Ruscio, D. (2021). Recommending Third-party Library Updates with LSTM Neural Networks. The 11th Italian Information Retrieval Workshop, 2021. URL https://ceur-ws.org/Vol-2947/paper7.pdf. This work presented a first version of DeepLib, a LSTM-based recommender system for migrating third-party libraries.