Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Mutation Testing Framework for Machine Learning

Version 1 : Received: 19 February 2021 / Approved: 23 February 2021 / Online: 23 February 2021 (09:22:02 CET)

How to cite: Singh, R. Mutation Testing Framework for Machine Learning. Preprints 2021, 2021020503 (doi: 10.20944/preprints202102.0503.v1). Singh, R. Mutation Testing Framework for Machine Learning. Preprints 2021, 2021020503 (doi: 10.20944/preprints202102.0503.v1).

Abstract

This is an article or technical note which is intended to provides an insight journey of Machine Learning Systems (MLS) testing, its evolution, current paradigm and future work. Machine Learning Models, used in critical applications such as healthcare industry[1], Automobile [2], [3] and Air Traffic control, Share Trading etc., and failure of ML Model can lead to severe consequences in terms of loss of life or property. To remediate this, developers, scientists, and ML community around the world, must build a highly reliable test architecture for critical ML application. At the very foundation layer, any test model must satisfy the core testing attributes such as test properties and its components. This attribute comes from the software engineering [5], [6], but the same cannot be applied in as-is form to the ML testing and we will tell you “why”.

Keywords

Machine Learning; Software Testing; Quality Attributes; Deep Learning; Model Mutation testing; DNN; DL

Subject

MATHEMATICS & COMPUTER SCIENCE, Algebra & Number Theory

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