Luca Pascarella

1.2k total citations · 1 hit paper
42 papers, 822 citations indexed

About

Luca Pascarella is a scholar working on Information Systems, Software and Artificial Intelligence. According to data from OpenAlex, Luca Pascarella has authored 42 papers receiving a total of 822 indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Information Systems, 14 papers in Software and 13 papers in Artificial Intelligence. Recurrent topics in Luca Pascarella's work include Software Engineering Research (29 papers), Software Testing and Debugging Techniques (12 papers) and Software System Performance and Reliability (11 papers). Luca Pascarella is often cited by papers focused on Software Engineering Research (29 papers), Software Testing and Debugging Techniques (12 papers) and Software System Performance and Reliability (11 papers). Luca Pascarella collaborates with scholars based in Switzerland, Netherlands and Italy. Luca Pascarella's co-authors include Alberto Bacchelli, Fabio Palomba, Gabriele Bavota, Antonio Mastropaolo, Magiel Bruntink, Simone Scalabrino, Davide Spadini, Rocco Oliveto, Denys Poshyvanyk and Massimiliano Di Penta and has published in prestigious journals such as Journal of Systems and Software, Empirical Software Engineering and Proceedings of the ACM on Human-Computer Interaction.

In The Last Decade

Luca Pascarella

37 papers receiving 811 citations

Hit Papers

Using pre-trained models to boost code review automation 2022 2026 2023 2024 2022 25 50 75 100
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Using pre-trained models to boost code review automation
    2022 102 citations Antonio Mastropaolo, Luca Pascarella et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Luca Pascarella Switzerland 16 654 406 222 206 122 42 822
Qiao Huang China 13 633 1.0× 302 0.7× 231 1.0× 208 1.0× 111 0.9× 27 761
Rabe Abdalkareem Canada 12 525 0.8× 192 0.5× 154 0.7× 173 0.8× 120 1.0× 31 655
Juri Di Rocco Italy 16 501 0.8× 318 0.8× 280 1.3× 164 0.8× 52 0.4× 70 697
Kenny Wong Canada 12 526 0.8× 225 0.6× 288 1.3× 210 1.0× 90 0.7× 36 657
Maurício Aniche Netherlands 17 636 1.0× 368 0.9× 133 0.6× 215 1.0× 100 0.8× 39 781
Ana C. R. Paiva Portugal 15 372 0.6× 454 1.1× 123 0.6× 176 0.9× 181 1.5× 79 659
Csaba Nagy Switzerland 14 491 0.8× 226 0.6× 148 0.7× 193 0.9× 69 0.6× 43 570
Robert Merkel Australia 18 577 0.9× 926 2.3× 164 0.7× 192 0.9× 106 0.9× 41 1.1k
Iman Keivanloo Canada 20 1.1k 1.6× 622 1.5× 229 1.0× 245 1.2× 395 3.2× 55 1.2k
Rahul Pandita United States 13 528 0.8× 246 0.6× 145 0.7× 187 0.9× 328 2.7× 32 704

Countries citing papers authored by Luca Pascarella

Since Specialization
Citations

This map shows the geographic impact of Luca Pascarella's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Luca Pascarella with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Luca Pascarella more than expected).

Fields of papers citing papers by Luca Pascarella

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Luca Pascarella. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Luca Pascarella. The network helps show where Luca Pascarella may publish in the future.

Co-authorship network of co-authors of Luca Pascarella

This figure shows the co-authorship network connecting the top 25 collaborators of Luca Pascarella. A scholar is included among the top collaborators of Luca Pascarella based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Luca Pascarella. Luca Pascarella is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
D’Agostino, Francesco, Flaminio Ferrara, Claudio Gennarelli, et al.. (2025). 3-D Probe Positioning Errors Compensation in a Non-Redundant Spherical NF-FF Transformation. 1–5.
2.
Pascarella, Luca, et al.. (2024). The NLBSE'24 Tool Competition. CINECA IRIS Institutial research information system (University of Pisa). 33–40. 2 indexed citations
3.
4.
Capozzoli, Amedeo, Claudio Curcio, Francesco D’Agostino, Angelo Liseno, & Luca Pascarella. (2024). Legendre Quadrature for the Discretization of 2D Radiating Panels. 1101–1102.
5.
Pascarella, Luca, et al.. (2024). Gaze-Guided Semi-Autonomous Quadruped Robot for Enhanced Assisted Living. Open MIND. 1–6. 2 indexed citations
6.
Pascarella, Luca, et al.. (2024). Towards Real-Time Fast Unmanned Aerial Vehicle Detection Using Dynamic Vision Sensors. Open MIND. 1–6. 3 indexed citations
7.
Mastropaolo, Antonio, et al.. (2024). Towards Summarizing Code Snippets Using Pre-Trained Transformers. 1–12. 3 indexed citations
8.
Mastropaolo, Antonio, Emad Aghajani, Luca Pascarella, & Gabriele Bavota. (2023). Automated variable renaming: are we there yet?. Empirical Software Engineering. 28(2). 6 indexed citations
9.
Pascarella, Luca & Michele Magno. (2023). Grayscale and Event-Based Sensor Fusion for Robust Steering Prediction for Self-Driving Cars. 1–6. 2 indexed citations
10.
Capozzoli, Amedeo, Claudio Curcio, Francesco D’Agostino, Angelo Liseno, & Luca Pascarella. (2023). Optimized discretization of 1D radiating panels. 1–4. 1 indexed citations
11.
Izadi, Maliheh, et al.. (2023). The NLBSE'23 Tool Competition. Research Repository (Delft University of Technology). 1–8. 7 indexed citations
12.
13.
Mastropaolo, Antonio, Luca Pascarella, & Gabriele Bavota. (2022). Using deep learning to generate complete log statements. 2279–2290. 49 indexed citations
14.
Pascarella, Luca, Magiel Bruntink, & Alberto Bacchelli. (2019). Classifying code comments in Java software systems. Empirical Software Engineering. 24(3). 1499–1537. 38 indexed citations
15.
Pascarella, Luca, Fabio Palomba, Massimiliano Di Penta, & Alberto Bacchelli. (2018). How Is Video Game Development Different from Software Development in Open Source?. Zenodo (CERN European Organization for Nuclear Research). 1 indexed citations
16.
Malavolta, Ivano, et al.. (2018). A graph-based dataset of commit history of real-world Android apps. VU Research Portal. 30–33. 29 indexed citations
17.
Pascarella, Luca, Fabio Palomba, & Alberto Bacchelli. (2018). Re-evaluating Method-Level Bug Prediction. Zenodo (CERN European Organization for Nuclear Research). 6 indexed citations
18.
Pascarella, Luca, Davide Spadini, Fabio Palomba, Magiel Bruntink, & Alberto Bacchelli. (2018). Information Needs in Contemporary Code Review. Proceedings of the ACM on Human-Computer Interaction. 2(CSCW). 1–27. 90 indexed citations
19.
Pascarella, Luca. (2018). Classifying code comments in Java mobile applications. Research Repository (Delft University of Technology). 39–40. 5 indexed citations
20.
Pascarella, Luca & Alberto Bacchelli. (2017). Classifying Code Comments in Java Open-Source Software Systems. Zurich Open Repository and Archive (University of Zurich). 227–237. 61 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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