update entries

claudioDsi · Jul 22, 2024 · 5ddc582 · 5ddc582
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@@ -103,14 +103,14 @@ <h3>Current role: Post-doc researcher at University of L'Aquila</h3>
                 <ul>
                   <li><i class="bi bi-chevron-right"></i> <strong>Birthday:</strong> <span>10 January 1994</span></li>
                   <li><i class="bi bi-chevron-right"></i> <strong>City:</strong> <span>University of L'Aquila</span></li>
+                  <li><i class="bi bi-chevron-right"></i> <strong>Degree:</strong> <span>Ph.D.in Computer Science</span></li>
                 </ul>
               </div>
               <div class="col-lg-6">
-                <ul>
-
-                  <li><i class="bi bi-chevron-right"></i> <strong>Degree:</strong> <span>Ph.D.in Computer Science</span></li>
+                <ul>    
                   <li><i class="bi bi-chevron-right"></i> <strong>Contact email:</strong> <span>[email protected]</span></li>
-
+                  <li><i class="bi bi-chevron-right"></i> <strong>Twitter account:</strong> <span><a href="https://x.com/DsiClaudio">DsiClaudio</a></span></li>
+                  <li><i class="bi bi-chevron-right"></i> <strong>ORCID:</strong> <span> <a href="https://orcid.org/0000-0001-9872-9542">0000-0001-9872-9542</a></span></li>
                 </ul>
               </div>
             </div>
@@ -142,22 +142,27 @@ <h5><b>Journal publication</b></h5>
 
             <ul>
 
-              <h6>2024</h6>
-              <li>Di Sipio, C., Di Rocco, J., Di Ruscio, D., & Nguyen, P. T. (2024). LEV4REC: A feature-based approach to engineering RSSEs. <i>Journal of Computer Languages, 78</i>.</li>
-              <li>Nguyen, P. T., Di Rocco, J., Di Sipio, C., Rubei, R., Di Ruscio, D., & Di Penta, M. (2024). GPTSniffer: A CodeBERT-based classifier to detect source code written by ChatGPT. <i>Journal of Systems and Software, 112059</i>.</li>
+              <h6><b>2024</b></h6>
+              <li>Di Sipio, C., Di Rocco, J., Di Ruscio, D., and Nguyen, P. T. (2024). LEV4REC: A feature-based approach to engineering RSSEs. <i>Journal of Computer Languages, 78, 101256. <a href="https://doi.org/10.1016/j.cola.2023.101256">https://doi.org/10.1016/j.cola.2023.101256</a> </i>.</li>
+              <li>Nguyen, P. T., Di Rocco, J., Di Sipio, C., Rubei, R., Di Ruscio, D., & Di Penta, M. (2024). GPTSniffer: A CodeBERT-based classifier to detect source code written by ChatGPT. <i>Journal of Systems and Software, 112059. DOI: <a href="https://doi.org/10.1016/j.jss.2024.112059">https://doi.org/10.1016/j.jss.2024.112059</a> </i>.</li>
+
+              <h6><b>2023</b></h6>
+              <li>Di Sipio, C., Di Rocco, J., Di Ruscio, D. and Nguyen, P.T.. MORGAN: a modeling recommender system based on graph kernel. <i>Softw Syst Model</i>, 22, 1427–1449 (2023). DOI: <a href="https://doi.org/10.1007/s10270-023-01102-8">https://doi.org/10.1007/s10270-023-01102-8</a>.</li>
+
+              <li>Sas, C., Capiluppi, A., Di Sipio, C., Di Rocco, J., and Di Ruscio, D. (2023). GitRanking: A ranking of GitHub topics for software classification using active sampling. Software: Practice and Experience, 53(10), 1982–2006. <a href="https://doi.org/10.1002/spe.3238">https://doi.org/10.1002/spe.3238</a> </li>
 
-              <h6>2022</h6>
+              <h6><b>2022</b></h6>
               <li>Di Rocco, J., Di Ruscio, D., Di Sipio, C., Nguyen, P.T., and Pierantonio, A. (2022). MemoRec: a recommender system for assisting modelers in specifying metamodels. <i>Softw Syst Model</i>. DOI: <a href="https://doi.org/10.1007/s10270-022-00994-2">https://doi.org/10.1007/s10270-022-00994-2</a>.</li>
               <li>Nguyen, P.T., Di Rocco, J., Rubei, R., Di Sipio C., and Di Ruscio, D. (2022). DeepLib: Machine translation techniques to recommend upgrades for third-party libraries. <i>Expert Systems with Applications, Volume 202, 117267</i>, ISSN 0957-4174. DOI: <a href="https://doi.org/10.1016/j.eswa.2022.117267">https://doi.org/10.1016/j.eswa.2022.117267</a>.</li>
               <li>Di Rocco, J., Di Ruscio, D., Di Sipio, C., Nguyen, P.T., and Rubei, R. (2022). HybridRec: A recommender system for tagging GitHub repositories. <i>Applied Intelligence</i>. DOI: <a href="https://doi.org/10.1007/s10489-022-03864-y">https://doi.org/10.1007/s10489-022-03864-y</a>.</li>
               <li>Rubei, R., Di Ruscio, D., Di Sipio, C, Di Rocco J., and Nguyen, P.T. (2022). Providing upgrade plans for third-party libraries: a recommender system using migration graphs. <i>Appl.Intell, 52</i>, 12000–12015. DOI: <a href="https://doi.org/10.1007/s10489-021-02911-4">https://doi.org/10.1007/s10489-021-02911-4</a>.</li>
               <li>Nguyen, P. T., Di Sipio, C., Di Rocco, J., Di Penta, M., and Di Ruscio, D. (2022). Fitting Missing API Puzzles with Machine Translation Techniques. <i>Journal of Expert Systems With Applications</i>. DOI: <a href="https://doi.org/10.1016/j.eswa.2022.119477">https://doi.org/10.1016/j.eswa.2022.119477</a>.</li>
 
-              <h6>2021</h6>
+              <h6><b>2021</b></h6>
               <li>Di Rocco, J., Di Ruscio, D., Di Sipio, C., Nguyen, P.T., and Rubei, R. (2021). Development of recommendation systems for software engineering: the CROSSMINER experience. <i>Empirical Software Engineering, 26(4)</i>:1–40. DOI: <a href="https://doi.org/10.1007/s10664-021-09963-7">https://doi.org/10.1007/s10664-021-09963-7</a>.</li>
               <li>Nguyen, P.T., Di Rocco, J., Di Sipio, C., Di Ruscio, D., and Di Penta, M. (2021). Recommending API function calls and code snippets to support software development. <i>IEEE Transactions on Software Engineering</i>, pages 1–1. DOI: <a href="https://doi.org/10.1109/TSE.2021.3059907">https://doi.org/10.1109/TSE.2021.3059907</a>.</li>
 
-              <h6>2020</h6>
+              <h6><b>2020</b></h6>
               <li>Duong, L. T., Nguyen, P. T., Di Sipio, C., and Di Ruscio, D. (2020). Automated fruit recognition using EfficientNet and MixNet. <i>Computers and Electronics in Agriculture, Volume 171</i>, 105326, ISSN 0168-1699. DOI: <a href="https://doi.org/10.1016/j.compag.2020.105326">https://doi.org/10.1016/j.compag.2020.105326</a>.</li>
               <li>Rubei, R., Di Sipio, C., Nguyen, P.T., Di Rocco, J., and Di Ruscio, D. (2020). PostFinder: Mining Stack Overflow posts to support software developers. <i>Information and Software Technology, Volume 127</i>, 106367, ISSN 0950-5849. DOI: <a href="https://doi.org/10.1016/j.infsof.2020.106367">https://doi.org/10.1016/j.infsof.2020.106367</a>.</li>
 
@@ -170,26 +175,29 @@ <h6>2020</h6>
       <h5><b>Conference publications</b></h5>
       <ul>
 
-        <h6>2024</h6>
-        <li>Cederbladh, J., Berardinelli, L., Bilic, D., Bruneliere, H., Cicchetti, A., Dehghani, M., Di Sipio C., Miranda, J.P, Rahimi A., and Rubei, R. (2024, April). Towards Automating Model-Based Systems Engineering in Industry-An Experience Report. In The 18th Annual IEEE International Systems Conference (SYSCON 2024).</li>
-        <li>Weyssow, M., Di Sipio, C., Di Ruscio, D., and Sahraoui, H. (2024). CodeLL: A Lifelong Learning Dataset to Support the Co-Evolution of Data and Language Models of Code. arXiv preprint arXiv:2312.12492. To be appeared in MSR 2024 proceedings.</li>
-        <li>D'Angelo, A., Di Sipio, C., Politowsky, C., and Rubei, R. (2024). PlayMyData: a curated dataset of multi-platform video games. arXiv preprint arXiv:2401.08561. To be appeared in MSR 2024 proceedings.</li>
-
-        <h6>2023</h6>
-        <li>Di Sipio, C., Di Rocco, J., Di Ruscio, D. et al. MORGAN: a modeling recommender system based on graph kernel. <i>Softw Syst Model</i>, 22, 1427–1449 (2023). DOI: <a href="https://doi.org/10.1007/s10270-023-01102-8">https://doi.org/10.1007/s10270-023-01102-8</a>.</li>
+        <h6><b>2024</b></h6>
+        <li>Di Sipio, C., Rubei, R., Di Rocco, J., Di Ruscio, D., and Nguyen, P. T. (2024b). Automated categorization of pre-trained models in software engineering: A case study with a Hugging Face dataset. Proceedings of the 28th International Conference on Evaluation and Assessment in Software Engineering, 351–356. <a href="https://doi.org/10.1145/3661167.3661215">https://doi.org/10.1145/3661167.3661215</a> </li>
+
+        <li>Cederbladh, J., Berardinelli, L., Bilic, D., Bruneliere, H., Cicchetti, A., Dehghani, M., Di Sipio C., Miranda, J.P, Rahimi A., and Rubei, R. (2024, April). Towards Automating Model-Based Systems Engineering in Industry-An Experience Report. In The 18th Annual IEEE International Systems Conference (SYSCON 2024). DOI: <a href="https://doi.org/10.1109/SysCon61195.2024.10553610">https://doi.org/10.1109/SysCon61195.2024.10553610</a></li>
+        <li>Weyssow, M., Di Sipio, C., Di Ruscio, D., and Sahraoui, H. (2024). CodeLL: A Lifelong Learning Dataset to Support the Co-Evolution of Data and Language Models of Code. . 2024 IEEE/ACM 21st International Conference on Mining Software Repositories (MSR), 637–641. DOI: <a href="https://ieeexplore.ieee.org/abstract/document/10555636">https://ieeexplore.ieee.org/abstract/document/10555636</a></li>
+        <li>D'Angelo, A., Di Sipio, C., Politowsky, C., and Rubei, R. (2024). PlayMyData: a curated dataset of multi-platform video games.Proceedings of the 21st International Conference on Mining Software Repositories, 525–529. DOI: <a href="https://doi.org/10.1145/3643991.3644869">https://doi.org/10.1145/3643991.3644869</a></li>
+
+        <h6><b>2023</b></h6> 
+        <li>Nguyen, P. T., Rubei, R., Rocco, J. D., Di Sipio, C., Di Ruscio D., and Di Penta, M. (2023). Dealing with Popularity Bias in Recommender Systems for Third-party Libraries: How far Are We? 12–24. <a href="https://doi.org/10.1109/MSR59073.2023.00016">https://doi.org/10.1109/MSR59073.2023.00016</a> </li>
+
 
-        <h6>2022</h6>
+        <h6><b>2022</b></h6>
         <li>Rubei, R., Di Sipio, C., Di Rocco, J., Di Ruscio, D., and Nguyen, P.T. (2022). Endowing third-party libraries recommender systems with explicit user feedback mechanisms. In 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), pp. 817-821. DOI: <a href="https://doi.org/10.1109/SANER53432.2022.00099">https://doi.org/10.1109/SANER53432.2022.00099</a>.</li>
-        <li>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: <a href="#">#</a>.</li>
-        <li>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).</li>
+        <li>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: <a href="https://doi.org/10.1145/3550356.3552376">https://doi.org/10.1145/3550356.3552376</a>.</li>
+        <li>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). <a href="https://doi.org/10.1145/3550355.3552459">https://doi.org/10.1145/3550355.3552459</a></li>
 
-        <h6>2021</h6>
+        <h6><b>2021</b></h6>
         <li>Nguyen, P.T., Di Ruscio, D., Di Rocco, J., Di Sipio, C., and Di Penta, M. (2021). 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. Association for Computing Machinery. ISBN 9781450390538. DOI: <a href="https://doi.org/10.1145/3463274.3463809">https://doi.org/10.1145/3463274.3463809</a>.</li>
         <li>Di Rocco, J., Di Sipio, C., Di Ruscio, D., and Nguyen, P.T. (2021). A GNN-based Recommender System to Assist the Specification of Metamodels and Models. 2021 ACM/IEEE 24th International Conference on Model Driven Engineering Languages and Systems (MODELS), pp. 70-81. DOI: <a href="https://doi.org/10.1109/MODELS50736.2021.00016">10.1109/MODELS50736.2021.00016</a>.</li>
         <li>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: <a href="https://doi.org/10.1145/3460231.3478885">https://doi.org/10.1145/3460231.3478885</a>.</li>
         <li>Nguyen, P. T., Di Sipio, C, Di Rocco, J., Di Penta, M., and Di Ruscio, D. (2021). 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), pp. 253-265. DOI: <a href="https://doi.org/10.1109/ASE51524.2021.9678946">10.1109/ASE51524.2021.9678946</a>.</li>
 
-        <h6>2020</h6>
+        <h6><b>2020</b></h6>
         <li>Di Rocco, J., Di Ruscio, D., Di Sipio, C., Nguyen, P.T., and Rubei, R. (2020). 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. Association for Computing Machinery. ISBN 9781450375801. DOI: <a href="https://doi.org/10.1145/3382494.3410690">https://doi.org/10.1145/3382494.3410690</a>.</li>
         <li>Di Sipio, C., Rubei, R., Di Ruscio, D., and Nguyen, P.T. (2020). A multinomial naïve bayesian (mnb) network to automatically recommend topics for github repositories. In Proceedings of the Evaluation and Assessment in Software Engineering, EASE ’20, page 71–80, New York, NY, USA. Association for Computing Machinery. ISBN 9781450377317. DOI: <a href="https://doi.org/10.1145/3383219.3383227">https://doi.org/10.1145/3383219.3383227</a>.</li>
 
@@ -203,11 +211,11 @@ <h6>2020</h6>
   <h5><b>Workshop publications</b></h5>
   <ul>
 
-    <h6>2023</h6>
-    <li>Di Rocco, J. and Di Sipio, C. (2023). 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.</li>
-    <li>Clerissi, D., Di Rocco, J., Di Ruscio, D., Di Sipio, C., Ihirwe, F., Mariani, L., Micucci, D., Rossi, M.T., and Rubei, R. (2023). Supporting Early-Safety Analysis of IoT Systems by Exploiting Testing Techniques. The 5th International Workshop on Multi-Paradigm Modeling for Cyber-Physical Systems.</li>
+    <h6><b>2023</b></h6>
+    <li>Di Rocco, J. and Di Sipio, C. (2023). 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. DOI: <a href="https://doi.org/10.1109/MODELS-C59198.2023.00091">https://doi.org/10.1109/MODELS-C59198.2023.00091</a></li>
+    <li>Clerissi, D., Di Rocco, J., Di Ruscio, D., Di Sipio, C., Ihirwe, F., Mariani, L., Micucci, D., Rossi, M.T., and Rubei, R. (2023). Supporting Early-Safety Analysis of IoT Systems by Exploiting Testing Techniques. The 5th International Workshop on Multi-Paradigm Modeling for Cyber-Physical Systems. DOI: <a href="https://doi.org/10.1109/MODELS-C59198.2023.00089">https://doi.org/10.1109/MODELS-C59198.2023.00089</a></li>
 
-    <h6>2021</h6>
+    <h6><b>2021</b></h6>
     <li>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: <a href="https://ceur-ws.org/Vol-2960/paper5.pdf">https://ceur-ws.org/Vol-2960/paper5.pdf</a>.</li>
     <li>Rubei, R., and Di Sipio, C. (2021). AURYGA: A Recommender System for Game Tagging. The 11th Italian Information Retrieval Workshop, 2021. URL: <a href="https://ceur-ws.org/Vol-2947/paper10.pdf">https://ceur-ws.org/Vol-2947/paper10.pdf</a>.</li>
     <li>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: <a href="https://ceur-ws.org/Vol-2947/paper7.pdf">https://ceur-ws.org/Vol-2947/paper7.pdf</a>. This work presented a first version of DeepLib, a LSTM-based recommender system for migrating third-party libraries.</li>