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Alessio Ferrari
Alessio Ferrari
Research Scientist, ISTI CNR
Verified email at isti.cnr.it - Homepage
Title
Cited by
Cited by
Year
Natural language processing for requirements engineering: A systematic mapping study
L Zhao, W Alhoshan, A Ferrari, KJ Letsholo, MA Ajagbe, EV Chioasca, ...
ACM Computing Surveys (CSUR) 54 (3), 1-41, 2021
1372021
Pure: A dataset of public requirements documents
A Ferrari, GO Spagnolo, S Gnesi
2017 IEEE 25th International Requirements Engineering Conference (RE), 502-505, 2017
1052017
Ambiguity and tacit knowledge in requirements elicitation interviews
A Ferrari, P Spoletini, S Gnesi
Requirements Engineering 21 (3), 333-355, 2016
1002016
Model checking interlocking control tables
A Ferrari, G Magnani, D Grasso, A Fantechi
FORMS/FORMAT 2010: Formal Methods for Automation and Safety in Railway and …, 2011
982011
A guidelines framework for understandable BPMN models
F Corradini, A Ferrari, F Fornari, S Gnesi, A Polini, B Re, GO Spagnolo
Data & Knowledge Engineering 113, 129-154, 2018
932018
Natural language processing for requirements engineering: The best is yet to come
F Dalpiaz, A Ferrari, X Franch, C Palomares
IEEE software 35 (5), 115-119, 2018
912018
Mining commonalities and variabilities from natural language documents
A Ferrari, GO Spagnolo, F Dell'Orletta
Proceedings of the 17th International Software Product Line Conference, 116-120, 2013
842013
Using NLP to detect requirements defects: An industrial experience in the railway domain
B Rosadini, A Ferrari, G Gori, A Fantechi, S Gnesi, I Trotta, S Bacherini
Requirements Engineering: Foundation for Software Quality: 23rd …, 2017
782017
Measuring and improving the completeness of natural language requirements
A Ferrari, F dell’Orletta, GO Spagnolo, S Gnesi
Requirements Engineering: Foundation for Software Quality: 20th …, 2014
632014
Natural Language Requirements Processing: A 4D Vision.
A Ferrari, F Dell'Orletta, A Esuli, V Gervasi, S Gnesi
IEEE Softw. 34 (6), 28-35, 2017
612017
Detecting requirements defects with NLP patterns: an industrial experience in the railway domain
A Ferrari, G Gori, B Rosadini, I Trotta, S Bacherini, A Fantechi, S Gnesi
Empirical Software Engineering 23, 3684-3733, 2018
572018
An NLP approach for cross-domain ambiguity detection in requirements engineering
A Ferrari, A Esuli
Automated Software Engineering 26 (3), 559-598, 2019
562019
Detecting domain-specific ambiguities: an NLP approach based on Wikipedia crawling and word embeddings
A Ferrari, B Donati, S Gnesi
2017 IEEE 25th International Requirements Engineering Conference Workshops …, 2017
552017
Using collective intelligence to detect pragmatic ambiguities
A Ferrari, S Gnesi
2012 20th IEEE International Requirements Engineering Conference (RE), 191-200, 2012
542012
On the industrial uptake of formal methods in the railway domain
D Basile, MH ter Beek, A Fantechi, S Gnesi, F Mazzanti, A Piattino, ...
International Conference on Integrated Formal Methods, 20--29, 0
47*
Pragmatic ambiguity detection in natural language requirements
A Ferrari, G Lipari, S Gnesi, GO Spagnolo
2014 IEEE 1st International Workshop on Artificial Intelligence for …, 2014
452014
Model-based development and formal methods in the railway industry
A Ferrari, A Fantechi, S Gnesi, G Magnani
IEEE software 30 (3), 28-34, 2013
402013
The metrô rio case study
A Ferrari, A Fantechi, G Magnani, D Grasso, M Tempestini
Science of Computer Programming 78 (7), 828-842, 2013
392013
Using machine learning to predict soil bulk density on the basis of visual parameters: Tools for in-field and post-field evaluation
G Bondi, R Creamer, A Ferrari, O Fenton, D Wall
Geoderma 318, 137-147, 2018
362018
Ten diverse formal models for a CBTC automatic train supervision system
F Mazzanti, A Ferrari
arXiv preprint arXiv:1803.10324, 2018
352018
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