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.

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. Paolo Tonella teaches Information Modeling and Analysis at the Master in Data and Software Engineering.

Paolo Tonella has given foundational contributions to Software Engineering, in the area of code analysis and testing. His ICSE Most Influential Paper (MIP) award winning paper, Analysis and Testing of Web Applications, initiated a new stream of research devoted to the development of testing techniques for web applications. His comprehensive book Reverse Engineering of Object-Oriented Code laid the foundations for the reverse engineering of object-oriented systems. His ISSTA 2004 paper Evolutionary Testing of Classes is recognized as a milestone for the automated generation of object oriented test cases. One of the most widely used Java test case generators, EvoSuite, can be traced back to this seminal ISSTA paper and to the associated tool, eToc.