Title: Misconceptions in deep learning testing
Deep learning testing is a new, recent area of software testing that is gaining increasing attention because of the widespread adoption of deep learning components to solve complex problems, such as image and natural language processing. This new area is developing its own language, methodology and toolset, often inheriting notions and approaches that proved to be successful for testing of traditional software. However, many such notions and approaches need to be revisited and reformulated to account for the specific features of deep learning components.
In this talk, I will consider the following questions associated with key, basic deep learning testing notions: What is a bug? What is a valid test input? Are Deep learning components deterministic? When is a fault detected? When is a mutant killed? What is a unique failure? I will give my answers to these questions, showing that adopting one definition or another has major implications on the conclusions we can draw about the effectiveness of novel testing techniques, or lack thereof.
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. He has written over 150 peer reviewed conference papers and over 50 journal papers. His H-index (according to Google scholar) is 60. 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, simulation based testing, and test oracle generation and improvement.
Title: Current trends in quantum software testing.
Although Quantum Software may be used as third-party component, the hardware characteristics where the software is executed require running the quantum program multiple times. The difficulty of accessing quantum computers, the small number of available quantum computers and, overall, the high costs of using them, invites to think about different models for testing quantum programs. This talk will deal with this problem, both in its adiabatic (circuit-based) as in its annealing point of view. Thus, the talk will analyze the possibility of testing quantum programs before deploying them to actual quantum computers, both on the adiabatic and on the annealing points of view. The speaker will present some testing techniques for testing circuit-based quantum programs, as well as a progressive methodology for developing and testing quantum annealing problems.
Macario Polo Usaola is a full Professor of Computer Science at the University of Castilla-La Mancha. His main research areas are related to the automation of the Software Engineering process, specially testing. Since a few years ago, he has been involved in several research projects about Quantum Computing, both in its adiabatic as in its annealing perspectives. In this sense, he is developing models and techniques to automate Quantum Software testing.
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