Fabiano Pecorelli

Postdoctoral Researcher

About Me

I am a Postdoctoral Researcher in the Software Engineering Salerno (SeSa) Research Lab at the University of Salerno, Italy. Before, I have been a Postdoctoral Researcher in the Jheronimus Academy of Data Engineering (JADE) lab at Jheronimus Academy of Data Science (JADS), Eindhoven University of Technology, The Netherlands and in the Cloud Software Evolution and Assessment (CloudSEA) research group at Tampere University, Finland. In 2022, I received the European Ph.D. Degree, magna cum laude, in Computer Science from the University of Salerno, Italy, advised by Prof. Andrea De Lucia, working in the SeSa Lab (Software Engineering Salerno). In 2018, I received the Master's Degree, magna cum laude, in Computer Science from the University of Salerno, Italy, advised by Prof. Filomena Ferrucci. In 2016, I received the Bachelor's Degree, magna cum laude, in Computer Science from University of Salerno, Italy, advised by Prof. Andrea De Lucia.



My research interests include Software Code and Test Quality, Predictive Analytics, Mining Software Repositories, Software Maintenance and Evolution, Empirical Software Engineering, and Quantum Software Engineering. I'm actively serving as a referee for various international journals in the field of Software Engineering (e.g., TOSEM, EMSE, JSS).

Let's connect

Email: fpecorelli@unisa.it
Twitter: @FabianoPecorel1
Skype: fabiano.pecorelli

Resume

Curriculum Vitae

Education

November 2022 - Until now

Postdoctoral Researcher - Jheronimus Academy of Data Science

Postdoctoral Researcher in the Jheronimus Academy of Data Engineering (JADE) lab at Jheronimus Academy of Data Science (JADS), Eindhoven University of Technology, The Netherlands.

November 2021 - October 2022

Postdoctoral Researcher - Tampere University

Postdoctoral Researcher in the Cloud Software Evolution and Assessment (CloudSEA) research group at Tampere University, Finland.

November 2018 - March 2022

Ph.D. Student - University of Salerno

Ph.D. Thesis about Technical Debt in Software Engineering: "Technical Debt in Software Engineering - A Multi-Perspective Investigation", under the supervision of Professor Andrea De Lucia (University of Salerno).

March 2019 - July 2019

Visiting Period - University of Zurich (UZH)

Visiting Ph.D Student at Zurich Empirical Software Engineering Team (ZEST), under the supervision of Professor Alberto Bacchelli.

March 2018 - July 2018

Visiting Period - University College of London (UCL)

Visiting ERASMUS Student at Centre for Research on Evolution, Search and Testing (CREST), under the supervision of Professor Federica Sarro.

September 2016 - September 2018

Master Degree Cum Laude – University of Salerno

Master Thesis in Search Based Software Engineering (SBSE): "A Distibuted Multi-Objective Genetic Algorithm based on jMetal and Spark: A preliminary investigation", under the supervision of Professor Filomena Ferrucci (University of Salerno) and Dr. Federica Sarro (University College of London).

September 2013 - September 2016

Bachelor Degree Cum Laude - University of Salerno

Bachelor Thesis in Software Engineering: "A Machine-Learning based tool for Bug Prediction", under the supervision of Professor Andrea De Lucia (University of Salerno).

Experience

March 2022 - June 2022

Teaching Assistant- Academic Writing and Publishing

Teaching Assistant in the Academic Writing and Publishing course (Ph.D. degree) at Tampere University, with Professor Davide Taibi.

March 2022 - June 2022

Teaching Assistant - Programming 3

Teaching Assistant in the Programming 3 course (Bachelor's degree) at Tampere University, with Professor Terhi Kilamo.

November 2021 - February 2022

Teaching Assistant - DevOps

Teaching Assistant in the DevOps course (Bachelor's degree) at Tampere University, with Professor Kari Systä.

November 2018 - March 2022

Teaching Assistant - Software Maintenance and Evolution

Teaching Assistant in the Software Maintenance and Evolution course (Master's degree) at the University of Salerno, with Professor Andrea De Lucia.

November 2018 - March 2022

Teaching Assistant - Software Engineering

Teaching Assistant in the Software Engineering course (Bachelor's degree) at the University of Salerno, with Professor Andrea De Lucia.

skills

Download my cv
  • Research Interests:
  • My research interests include Software Code and Test Quality, Predictive Analytics, Mining Software Repositories, Software Maintenance and Evolution, Empirical Software Engineering, and Quantum Software Engineering.
  • Languages
    • English (Fluent Speaking)
    • Spanish (Basic Knowledge)
    • Italian (Native)
  • Hobbies & Interests
    • Music
    • Sport
    • Travel
    • Cooking

Publications

My Works

Conferences

Journals

Workshops

Comparing Machine Learning and Heuristic Approaches for Metric-Based Code Smell Detection.

F. Pecorelli, F. Palomba, D. Di Nucci, A. De Lucia

In Proceedings of the IEEE/ACM International Conference on Program Comprehension (ICPC 2019) [pdf]


Test-related factors and post-release defects: an empirical study.

F. Pecorelli

In Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, (ESEC/FSE 2019) [pdf]


Refactoring Android-specific Energy Smells: A Plugin for Android Studio.

E. Iannone, F. Pecorelli, D. Di Nucci, F. Palomba, A. De Lucia

In Proceedings of the 28th IEEE/ACM International Conference on Program Comprehension (ICPC 2020) [pdf]


Just-In-Time Test Smell Detection and Refactoring: The DARTS Project.

S. Lambiase, A. Cupito, F. Pecorelli, A. De Lucia, F. Palomba

In Proceedings of the 28th IEEE/ACM International Conference on Program Comprehension (ICPC 2020) [pdf]


Developer-Driven Code Smell Prioritization.

F. Pecorelli, F. Palomba, F. Khomh, A. De Lucia

In Proceedings of IEEE/ACM Working Conference on Mining Software Repositories (MSR 2020) [pdf]


Testing of Mobile Applications in the Wild: A Large-Scale Empirical Study on Android Apps.

F. Pecorelli, G. Catolino, F. Ferrucci, A. De Lucia, F. Palomba

In Proceedings of the 28th IEEE/ACM International Conference on Program Comprehension (ICPC 2020) [pdf]


Refactoring Recommendations Based on the Optimization of Socio-Technical Congruence.

M. De Stefano, F. Pecorelli, D. A. Tamburri, F. Palomba, A. De Lucia

In Proceedings of the 2020 IEEE International Conference on Software Maintenance and Evolution (ICSME 2020) [pdf]


VITRuM: A Plug-In for the Visualization of Test-Related Metrics.

F. Pecorelli, G. Di Lillo, F. Palomba, A. De Lucia

In Proceedings of the International Conference on Advanced Visual Interfaces (AVI 2020) [pdf]


cASpER: A Plug-in for Automated Code Smell Detection and Refactoring.

M. De Stefano, M. S. Gambardella, F. Pecorelli, F. Palomba, A. De Lucia

In Proceedings of the International Conference on Advanced Visual Interfaces (AVI 2020) [pdf]


PANDORA: Continuous mining software repository and dataset generation.

H. Nguyen, F. Lomio, F. Pecorelli, V. Lenarduzzi

In Proceedings of the IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER 2022) [pdf]


Predicting The Energy Consumption Level of Java Classes in Android Apps: An Exploratory Analysis.

E. Iannone, M. De Stefano, F. Pecorelli, A. De Lucia

In Proceedings of the 9th IEEE/ACM International Conference on Mobile Software Engineering and Systems (MobileSoft 2022) [pdf]

A Large Empirical Assessment of the Role of Data Balancing in Machine-Learning-based Code Smell Detection.

F. Pecorelli, D. Di Nucci, C. De Roover, A. De Lucia

Elsevier's Journal of Systems and Software (JSS) [pdf]



Adaptive selection of classifiers for bug prediction: A large-scale empirical analysis of its performances and a benchmark study.

F. Pecorelli, D. Di Nucci

Science of Computer Programming [pdf]



The Relation of Test-Related Factors to Software Quality: A Case Study on Apache Systems.

F. Pecorelli, F. Palomba, A. De Lucia

Empirical Software Engineering (EMSE) [pdf]



Software Testing and Android Applications: A Large-Scale Empirical Study.

F. Pecorelli, G. Catolino, F. Ferrucci, A. De Lucia, F, Palomba

Empirical Software Engineering (EMSE) [pdf]



On the Adequacy of Static Analysis Warnings with Respect to Code Smell Prediction.

F. Pecorelli, S. Lujan, V. Lenarduzzi, F. Palomba, A. De Lucia

Empirical Software Engineering (EMSE) [pdf]



Impacts of software community patterns on process and product: An empirical study.

M. De Stefano, E. Iannone, F. Pecorelli, D. A. Tamburri

Science of Computer Programming [pdf]



OSSARA: Abandonment Risk Assessment for Embedded Open Source Components.

X. Li, S. Moreschini, F. Pecorelli, D. Taibi

IEEE Software [pdf]



Software engineering for quantum programming: How far are we?.

M. De Stefano, F. Pecorelli, D. Di Nucci, F. Palomba, A. De Lucia

Elsevier's Journal of Systems and Software (JSS) [pdf]

On the Role of Data Balancing for Machine Learning-Based Code Smell Detection.

F. Pecorelli, D. Di Nucci, C. De Roover, A. De Lucia

In Proceedings of the 3rd ACM SIGSOFT International Workshop on Machine Learning Techniques for Software Quality Evaluation (MaLTeSQuE 2019), Tallinn, Estonia, 2019, 6 pages [pdf]


Splicing Community Patterns and Smells: A Preliminary Study.

M. De Stefano, F. Pecorelli, D. A. Tamburri, F. Palomba, A. De Lucia

In Proceedings of the 3rd ACM SIGSOFT International Workshop on Machine Learning Techniques for Software Quality Evaluation (MaLTeSQuE 2019), Tallinn, Estonia, 2019, 6 pages [pdf]


A preliminary study on the adequacy of static analysis warnings with respect to code smell prediction.

S. Lujan, F. Pecorelli, F. Palomba, A. De Lucia, V. Lenarduzzi

In Proceedings of the 4th ACM SIGSOFT International Workshop on Machine-Learning Techniques for Software-Quality Evaluation (MaLTeSQuE 2020), Virtual, 2020, 6 pages [pdf]


Comparing within-and cross-project machine learning algorithms for code smell detection.

M. De Stefano, F. Pecorelli, F. Palomba, A. De Lucia

In Proceedings of the 5th International Workshop on Machine Learning Techniques for Software Quality Evolution (MaLTeSQuE 2020), Virtual, 2021, 6 pages [pdf]