| 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 | 294 | 2021 |
| Pure: A dataset of public requirements documents A Ferrari, GO Spagnolo, S Gnesi 2017 IEEE 25th international requirements engineering conference (RE), 502-505, 2017 | 161 | 2017 |
| Ambiguity and tacit knowledge in requirements elicitation interviews A Ferrari, P Spoletini, S Gnesi Requirements Engineering 21 (3), 333-355, 2016 | 128 | 2016 |
| 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 | 123 | 2018 |
| 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 | 123 | 2018 |
| 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 | 104 | 2011 |
| 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 | 94 | 2013 |
| An NLP approach for cross-domain ambiguity detection in requirements engineering A Ferrari, A Esuli Automated Software Engineering 26 (3), 559-598, 2019 | 92 | 2019 |
| 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 | 87 | 2017 |
| Drivers, barriers and impacts of digitalisation in rural areas from the viewpoint of experts A Ferrari, M Bacco, K Gaber, A Jedlitschka, S Hess, J Kaipainen, ... Information and Software Technology 145, 106816, 2022 | 85 | 2022 |
| 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 (6), 3684-3733, 2018 | 82 | 2018 |
| 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 | 78 | 2014 |
| Natural language requirements processing: a 4D vision A Ferrari, F Dell'Orletta, A Esuli, V Gervasi, S Gnesi Ieee Software 34 (6), 28-35, 2017 | 74 | 2017 |
| 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 | 72 | 2017 |
| Using collective intelligence to detect pragmatic ambiguities A Ferrari, S Gnesi 2012 20th IEEE International Requirements Engineering Conference (RE), 191-200, 2012 | 63 | 2012 |
| 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 | 63* | |
| Teaching requirements elicitation interviews: an empirical study of learning from mistakes M Bano, D Zowghi, A Ferrari, P Spoletini, B Donati Requirements Engineering 24, 259-289, 2019 | 58 | 2019 |
| 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 | 52 | 2014 |
| 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 | 47 | 2013 |
| Formal methods in railways: a systematic mapping study A Ferrari, MHT Beek ACM Computing Surveys 55 (4), 1-37, 2022 | 44 | 2022 |
Stefania GnesiISTI-CNR, Italy在 isti.cnr.it 的电子邮件经过验证
