Keynote Talk

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Paolo Tonella

Università della Svizzera Italiana, Switzerland

Deep Learning Testing

Abstract: Deep neural networks have outperformed classical techniques in domains such as natural language processing, computer vision and speech recognition. They found several real world applications, ranging from autonomous vehicles to medical diagnosis. Correspondingly, the need for testing approaches to ensure their dependability and quality has increased.

In recent years, we have seen an exponential growth in the number of research papers that address various aspects of deep learning testing. In this seminar, I will describe a selected set of core problems in the field. In particular, I will focus on the reasons why such problems differ from the corresponding, traditional testing ones. I will present some of the solutions that appeared recently in the area and I will comment on the issues ("elephants in the room") that still affect the existing approaches.

Biography: Paolo Tonella is Full Professor at the Faculty of Informatics and at the Software Institute of Università della Svizzera Italiana (USI) in Lugano, Switzerland. He is Honorary Professor at University College London, UK and he is Affiliated Fellow of Fondazione Bruno Kessler, Trento, Italy, where he has been Head of Software Engineering until mid 2018. Paolo Tonella holds an ERC Advanced grant as Principal Investigator of the project PRECRIME. Paolo Tonella wrote over 150 peer reviewed conference papers and over 50 journal papers. His H-index (according to Google scholar) is 59. He is/was in the editorial board of the ACM Transactions on Software Engineering and Methodology, of the IEEE Transactions on Software Engineering, of Empirical Software Engineering, Springer, and of the Journal of Software: Evolution and Process, Wiley. His current research interests are in software testing, in particular approaches to ensure the dependability of machine learning based systems, automated testing of cyber physical systems, and test oracle inference and improvement.


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Rahul Gopinath

CISPA Helmholtz Center for Information Security, Germany

Learning and Refining Input Grammars for Effective Fuzzing

Abstract: TBA

Biography: Rahul Gopinath is an (incoming) lecturer at the University of Sydney. He is currently a postdoctoral researcher working on static and dynamic analysis of software at CISPA Saarland University. He received his Ph.D. in 2017 from the School of EECS at Oregon State University. He has worked as a postdoctoral scholar at CISPA Helmholtz center from 2017 onwards. At CISPA, his work is focused on fuzzing software systems. Fuzzing is essentially about evaluating how a software system responds to unexpected and possibly invalid inputs. Rahul is one of the authors of the Fuzzing Book. He has also worked on empirical evaluation of the effectiveness of different coverage techniques and Mutation Analysis.