%0 Conference Proceedings
%T Generating Biased Dataset for Metamorphic Testing of Machine Learning Programs
%+ National Institute of Informatics (NII)
%+ Swinburne University of Technology [Melbourne]
%A Nakajima, Shin
%A Chen, Tsong, Yueh
%Z Part 1: Test and Artificial Intelligence
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 31th IFIP International Conference on Testing Software and Systems (ICTSS)
%C Paris, France
%Y Christophe Gaston
%Y Nikolai Kosmatov
%Y Pascale Le Gall
%I Springer International Publishing
%3 Testing Software and Systems
%V LNCS-11812
%P 56-64
%8 2019-10-15
%D 2019
%R 10.1007/978-3-030-31280-0_4
%Z Computer Science [cs]
%Z Computer Science [cs]/Networking and Internet Architecture [cs.NI]Conference papers
%X Although both positive and negative testing are important for assuring quality of programs, generating a variety of test inputs for such testing purposes is difficult for machine learning software. This paper studies why it is difficult, and then proposes a new method of generating datasets that are test inputs to machine learning programs. The proposed idea is demonstrated with a case study of classifying hand-written numbers.
%G English
%Z TC 6
%Z WG 6.1
%2 https://inria.hal.science/hal-02526339/document
%2 https://inria.hal.science/hal-02526339/file/482770_1_En_4_Chapter.pdf
%L hal-02526339
%U https://inria.hal.science/hal-02526339
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-WG
%~ IFIP-TC6
%~ IFIP-WG6-1
%~ IFIP-ICTSS
%~ IFIP-LNCS-11812