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Please use this identifier to cite or link to this item: http://dspace.bits-pilani.ac.in:8080/jspui/xmlui/handle/123456789/8194
Title: A Framework for Mutation Testing of Machine Learning Systems
Authors: Rohil, Mukesh Kumar
Keywords: Computer Science
Learning
Software Testing
Quality Attributes
Deep Learning
Model Mutation Testing
Issue Date: 2021
Publisher: International Conference on Software Engineering and Knowledge Engineering
Abstract: In this paper, we provide an insight journey of Testing of Machine Learning Systems (MLS), its evolution, current paradigm, and we propose a machine learning mutation testing framework with scope for future work. Machine Learning (ML) Models are being used even in critical applications such as Healthcare, Automobile, Air Traffic control, Share Trading, etc., and failure of an ML Model can lead to severe consequences in terms of loss of life or property. To remediate this, the ML community around the world, must build highly reliable test architectures for critical ML applications. At the very foundation layer, any test model must satisfy the core testing attributes such as test properties and its components. These attributes should come from the software engineering discipline but the same cannot be applied in as-is form to the ML testing and in this paper, we explain why it is challenging to use Software Engineering Principles as-is when testing any MLS.
URI: https://ksiresearch.org/seke/seke21paper/paper155.pdf
http://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/8194
Appears in Collections:Department of Computer Science and Information Systems

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