dr. Dario Di Nucci

Bio

I am an assistant professor at the Jheronimus Academy Data and Engineering Lab of the Jheronimus Academy of Data Science, a collaboration between Tilburg University and Eindhoven Technical University. From 2018 to 2019 I have been a postdoctoral fellow at the Software Languages Lab of the Vrije Universiteit Brussel. In 2018 I received a PhD from the University of Salerno advised by Prof. Andrea De Lucia with a thesis entitled Methods and Tools for Focusing and Prioritizing the Testing Effort.

Expertise

My research is on empirical software engineering, in particular, software maintenance and evolution and software testing. To this aim, I apply several techniques such as machine learning, search-based algorithms, and mining of software repositories. I serve and had served as a program committee member of various international conferences (e.g., ESEC/FSE, ICSME, SANER, ICPC), and as a referee for various international journals in the field of software engineering (e.g., TSE, TOSEM, EMSE, JSS) and artificial intelligence (e.g., TKDE, Neurocomputing).

Courses

• Master's Thesis
• Data Engineering

Highlights

Scopus - Google Scholar - ResearchGate

Current Projects: RADON-H2020

Recent publications

1. Within-Project Defect Prediction of Infrastructure-as-Code Using Prod…

   Dalla Palma, S., Di Nucci, D., Palomba, F., & Tamburri, D. A. (2021). Within-Project Defect Prediction of Infrastructure-as-Code Using Product and Process Metrics. IEEE Transactions on Software Engineering. https://ieeexplore.ieee.org/document/9321740

2. The do's and don'ts of infrastructure code - A systematic gray litera…

   Kumara, I., Garriga, M., Romeu, A. U., Nucci, D. D., Tamburri, D. A., Heuvel, W-J. V. D., & Palomba, F. (2021). The do's and don'ts of infrastructure code: A systematic gray literature review. Information and Software Technology, 137, 106593. [106593].

3. Adaptive selection of classifiers for bug prediction: A large-scale e…

   Pecorelli, F., & Di Nucci, D. (2021). Adaptive selection of classifiers for bug prediction: A large-scale empirical analysis of its performances and a benchmark study. Science of Computer Programming.

4. DeepIaC: Deep learning-based linguistic anti-pattern detection in IaC

   Borovits, N., Weerasingha Dewage, I., Krishnan, P., Dalla Palma, S., Di Nucci, D., Palomba, F., Tamburri, D. A., & Van Den Heuvel, W-J. (2020). DeepIaC: Deep learning-based linguistic anti-pattern detection in IaC. In MaLTeSQuE 2020 - Proceedings of the 4th ACM SIGSOFT International Workshop on Machine-Learning Techniques for Software-Quality Evaluation, Co-located with ESEC/FSE 2020 (pp. 7-12). ACM.

5. Counterterrorism for Cyber-Physical Spaces: A computer vision approach

   Cascavilla, G., Slabber, J., Palomba, F., Di Nucci, D., Tamburri, D. A., & Van Den Heuvel, W-J. (2020). Counterterrorism for Cyber-Physical Spaces: A computer vision approach. In AVI '20: Proceedings of the International Conference on Advanced Visual Interfaces (pp. 1-5). [52] ACM.