publications

2022

1. [J3]

   Detecting Functional and Security-Related Issues in Smart Contracts: A Systematic Literature Review

   Valentina Piantadosi, Giovanni Rosa, Davide Placella, Simone Scalabrino, and Rocco Oliveto

   Software: Practice and experience 2022, To Appear
2. [C12]

   Fixing Dockerfile Smells: An Empirical Study

   Giovanni Rosa, Simone Scalabrino, and Rocco Oliveto

   ICSME (Registered Reports) 2022

   [doi] [pdf]
3. [C11]

   Assessing and Improving the Quality of Docker Artifacts

   Giovanni Rosa, Simone Scalabrino, and Rocco Oliveto

   ICSME 2022, To Appear
4. [C10]

   Sorry, I don’t Understand: Improving Voice User Interface Testing

   Emanuela Guglielmi, Giovanni Rosa, Simone Scalabrino, Gabriele Bavota, and Rocco Oliveto

   ASE 2022, To Appear

   [abs] [code] [web]

   Voice-based virtual assistants are becoming increasingly popular. Such systems provide frameworks to developers on which they can build their own apps. End-users can interact with such apps through a Voice User Interface (VUI), which allows to use natural language commands to perform actions. Testing such systems is far from trivial, especially due to the fact that the same command can be expressed using several semantically equivalent utterances, to which the VUI is expected to correctly react. To support developers in testing VUIs, Deep learning (DL)-based tools have been integrated in the development environments to generate paraphrases for selected seed utterances. This is for example the case of the Alexa Developer Console (ADC). Such tools, however, generate a limited number of paraphrases and do not allow to cover several corner cases. In this paper, we introduce VUI-UPSET, a novel approach that aims at adapting chatbot-testing approaches to VUI-testing. Indeed, both systems provide a similar natural-language-based interface to users. We conducted an empirical study to understand how VUI-UPSET compares to existing approaches in terms of (i) correctness of the generated paraphrases, and (ii) capability of revealing bugs. We manually analyzed 5,872 generated paraphrases, totaling 13,310 evaluations. Our results show that the DL-based tool integrated in the ADC generates a significantly higher percentage of meaningful paraphrases compared to VUI-UPSET. However, VUI-UPSET generates a higher number of bug-revealing paraphrases, which allows developers to test more thoroughly their apps at the cost of discarding a higher number of irrelevant paraphrases.

5. [C9]

   A Robust Approach for a Real-time Accurate Screening of ST Segment Anomalies

   Giovanni Rosa, Marco Russodivito, Gennaro Laudato, Angela Rita Colavita, Simone Scalabrino, and Rocco Oliveto

   HEALTHINF 2022

   [doi] [code] [slides]
6. [C8]

   Simulating the Doctor’s Behaviour: A Preliminary Study on the Identification of Atrial Fibrillation through Combined Analysis of Heart Rate and Beat Morphology

   Gennaro Laudato, Giovanni Rosa, Giovanni Capobianco, Angela Rita Colavita, Arianna Dal Forno, Fabio Divino, Claudio Lupi, Remo Pareschi, Stefano Ricciardi, Luca Romagnoli, Simone Scalabrino, Cecilia Tomassini, and Rocco Oliveto

   HEALTHINF 2022

   [doi]

2021

1. [J2]

   Tether: A Study on Bubble-Networks

   Giovanni Rosa, and Remo Pareschi

   Frontiers Blockchain 2021

   [doi] [pdf]
2. [C7]

   Automatic Real-time Beat-to-beat Detection of Arrhythmia Conditions

   Giovanni Rosa, Gennaro Laudato, Angela Rita Colavita, Simone Scalabrino, and Rocco Oliveto

   HEALTHINF 2021

   [doi] [pdf] [slides]
3. [C6]

   Evaluating SZZ Implementations Through a Developer-informed Oracle

   Giovanni Rosa, Luca Pascarella, Simone Scalabrino, Rosalia Tufano, Gabriele Bavota, Michele Lanza, and Rocco Oliveto

   ICSE 2021

   [doi] [pdf] [talk] [code] [slides]
4. [C5]

   A Multi-Class Approach for the Automatic Detection of Congestive Heart Failure in Windowed ECG

   Giovanni Rosa, Marco Russodivito, Gennaro Laudato, Simone Scalabrino, Angela Rita Colavita, and Rocco Oliveto

   MedInfo 2021

   [doi]

2020

1. [J1]

   Combining Rhythmic and Morphological ECG Features for Automatic Detection of Atrial Fibrillation: Local and Global Prediction Models

   Gennaro Laudato, Franco Boldi, Angela Rita Colavita, Giovanni Rosa, Simone Scalabrino, Aldo Lazich, and Rocco Oliveto

   BIOSTEC (Selected Papers) 2020

   [doi]
2. [C4]

   Combining Rhythmic and Morphological ECG Features for Automatic Detection of Atrial Fibrillation

   Gennaro Laudato, Franco Boldi, Angela Rita Colavita, Giovanni Rosa, Simone Scalabrino, Paolo Torchitti, Aldo Lazich, and Rocco Oliveto

   HEALTHINF 2020

   Best Student Paper Award

   [doi]
3. [C3]

   MIPHAS: Military Performances and Health Analysis System

   Gennaro Laudato, Giovanni Rosa, Simone Scalabrino, Jonathan Simeone, Francesco Picariello, Ioan Tudosa, Luca De Vito, Franco Boldi, Paolo Torchitti, Riccardo Ceccarelli, Fabrizio Picariello, Luca Torricelli, Aldo Lazich, and Rocco Oliveto

   HEALTHINF 2020

   [doi]
4. [C2]

   Does code review really remove coding convention violations?

   DongGyun Han, Chaiyong Ragkhitwetsagul, Jens Krinke, Matheus Paixão, and Giovanni Rosa

   SCAM 2020

   [doi]

2019

1. [C1]

   User Authentication through Keystroke Dynamics by means of Model Checking: A Proposal

   Fabio Di Tommaso, Michele Guerra, Fabio Martinelli, Francesco Mercaldo, Massimo Piedimonte, Giovanni Rosa, and Antonella Santone

   IEEE BigData 2019

   [doi]