Write a Blog >>
Thu 12 Nov 2020 08:13 - 08:14 at Virtual room 2 - ML Testing 2

To date, although machine learning has been successful in various practical applications, generic methods of testing machine learning code have not been established yet. Here we present a new approach to test machine learning code using the possible input region obtained as a \emph{polyhedron}. If an ML system generates different output for multiple input in the polyhedron, it is ensured that there exists a bug in the code. This property is known as one of theoretical fundamentals in statistical inference, for example, sparse regression models such as the lasso, and a wide range of machine learning algorithms satisfy this polyhedral condition, to which our testing procedure can be applied. We empirically show that the existence of bugs in lasso code can be effectively detected by our method in the \emph{mutation testing} framework.

Thu 12 Nov

Displayed time zone: (UTC) Coordinated Universal Time change

08:00 - 08:30
08:00
2m
Talk
DeepSearch: A Simple and Effective Blackbox Attack for Deep Neural Networks
Research Papers
Fuyuan Zhang MPI-SWS, Germany, Sankalan Pal Chowdhury MPI-SWS, Germany, Maria Christakis MPI-SWS
DOI
08:03
1m
Talk
Machine Learning Based Test Data Generation for Safety-critical Software
Paper Presentations
Ján Čegiň Faculty of Informatics and Information Technologies Slovak Technical University
08:05
1m
Talk
Machine Learning Testing: Survey, Landscapes and Horizons
Journal First
Jie M. Zhang University College London, UK, Mark Harman University College London, UK, Lei Ma Kyushu University, Yang Liu Nanyang Technological University, Singapore
08:07
1m
Talk
Machine Translation Testing via Pathological Invariance
Research Papers
Shashij Gupta IIT Bombay, India, Pinjia He ETH Zurich, Switzerland, Clara Meister ETH Zurich, Switzerland, Zhendong Su ETH Zurich
DOI
08:09
1m
Talk
Model-Based Exploration of the Frontier of Behaviours for Deep Learning System Testing
Research Papers
Vincenzo Riccio USI Lugano, Switzerland, Paolo Tonella USI Lugano, Switzerland
DOI
08:11
1m
Talk
PRODeep: A Platform for Robustness Verification of Deep Neural Networks
Tool Demos
Renjue Li Institute of Software at Chinese Academy of Sciences, China, Jianlin Li Institute of Software at Chinese Academy of Sciences, China, Cheng-Chao Huang Institute of Intelligent Software, China, Pengfei Yang Institute of Software at Chinese Academy of Sciences, China, Xiaowei Huang University of Liverpool, Lijun Zhang Institute of Software, Chinese Academy of Sciences, Bai Xue Institute of Software at Chinese Academy of Sciences, China, Holger Hermanns Saarland University
DOI
08:13
1m
Talk
Testing Machine Learning Code using Polyhedral Region
Visions and Reflections
Md Sohel Ahmed National Institute of Informatics, Japan, Fuyuki Ishikawa National Institute of Informatics, Mahito Sugiyama National Institute of Informatics, Japan
DOI
08:15
15m
Talk
Conversations on ML Testing 2
Paper Presentations
Fuyuan Zhang MPI-SWS, Germany, Ján Čegiň Faculty of Informatics and Information Technologies Slovak Technical University, Mark Harman University College London, UK, Renjue Li Institute of Software at Chinese Academy of Sciences, China, Shashij Gupta IIT Bombay, India, Vincenzo Riccio USI Lugano, Switzerland, M: Shin Yoo Korea Advanced Institute of Science and Technology