Ricercatore TD/b

Phuong Thanh Nguyen


Software Engineering; Recommender Systems; Machine Learning
06 Ingegneria del Software
INF/01 - Informatica
Computer Science and Operations Research

Curriculum   Scholar DBLP Scopus Research Gate


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:

Teaching (Academic year 2022 -- 2023):

Office hours (for receiving students):

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

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


I am a reviewer for the following conferences:


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