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I am an assistant professor (RTD/b) at the University of L’Aquila, Italy. I obtained a Ph.D. in Computer Science from the University of Jena, Germany. I was a postdoctoral researcher at Polytechnic University of Bari and the University of L’Aquila. My research interests include Computer Networks, Semantic Web, Recommender Systems, and Machine Learning. Recently, I have been working to develop recommender systems in Software Engineering for mining open source code repositories.

Research interests:

• Mining Software Repositories. Open-source software (OSS) forges, such as GitHub or Maven, offer many software projects that deliver stable and well-documented products. Most OSS forges typically sustain vibrant user and expert communities which in turn provide decent support, both for answering user questions and repairing reported software bugs. Moreover, OSS platforms are also an essential source of consultation for developers in their daily development tasks. Code reusing is an intrinsic feature of OSS, and developing new software by leveraging existing open source components allows one to considerably reduce their development effort. We have conceptualized techniques and tools to assist developers in their programming tasks.
• Recommender Systems. In online shopping platforms, recommender systems are considered to be an indispensable component, allowing business owners to offer personalized products to customers. The development of such systems has culminated in well-defined recommendation algorithms, which in turn prove their usefulness in other fields, such as entertainment industry, or employment-oriented service. Recommender systems in software engineering (RSSE) have been conceptualized on a comparable basis, i.e., they assist developers in navigating large information spaces and getting instant recommendations that are helpful to solve a particular development task. In this sense, RSSE provide developers with useful recommendations, which may consist of different items, such as code examples, topics, third-party components, to name a few.
• Machine Learning and Deep Learning. The proliferation of disruptive Machine Learning (ML) and especially Deep Learning (DL) algorithms has enabled a plethora of applications across several domains. Such techniques work on the basis of complex artificial neural networks, which are capable of effectively learning from data by means of a large number of parameters distributed in different network layers. In this way, they are able to simulate humans' cognitive functions, aiming to acquire real-world knowledge autonomously. ML/DL techniques are an advanced paradigm that brings in substantial improvement in performance compared to conventional learning algorithms. We have successfully studied and deployed various Machine Learning techniques in Software Engineering and other domains.

Academic service:

• Associate Editor of Springer Applied Intelligence (https://www.springer.com/journal/10489/editors).
• Member of the Editorial Board of the Journal of Universal Computer Science (https://bit.ly/3e8zm8c).
• Member of the Editorial Board of Elsevier Computers & Education: Artificial Intelligence (https://www.sciencedirect.com/journal/computers-and-education-artificial-intelligence/about/editorial-board).
• Regular reviewer for IEEE Transactions on Software Engineering, Elsevier Journal of Systems and Software.

Teaching (Academic year 2022 -- 2023):

• Machine Learning for Model-Driven Engineering (Master course)(https://univaq.coursecatalogue.cineca.it/insegnamenti/2022/37943/2017/9/10013?coorte=2021&schemaid=10597)

Office hours (for receiving students):

• Monday:  14h15 -- 16h15
• Tuesday: 09h15 -- 11h15

Edificio Alan Turing, Room 211, Via Vetoio snc., 67100 L'Aquila, Italy

Updates:

I am a reviewer for the following conferences:

• The Technical Track of the 20th International Conference on Mining Software Repositories (MSR 2023) (https://conf.researchr.org/committee/msr-2023/msr-2023-technical-papers-program-committee)
• The Technical Track of the ACM/IEEE International Conference on Technical Debt 2023 (https://conf.researchr.org/committee/TechDebt-2023/TechDebt-2023-technical-papers-technical-papers)
• The Special Track on AI for Social Impact (AISI), the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI 2023).
• The Research Papers Track of the 20th Joint meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering (ESEC/FSE 2023) (https://2023.esec-fse.org/committee/fse-2023-research-papers-program-committee).
• The Poster track of the 45th International Conference on Software Engineering (ICSE 2023) (https://conf.researchr.org/committee/icse-2023/icse-2023-posters-posters).
• The Artifact Evaluation track of the 45th International Conference on Software Engineering (ICSE 2023) (https://conf.researchr.org/committee/icse-2023/icse-2023-artifact-evaluation-artifact-evaluation).