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Machine Learning Data Suitability and Performance Testing Using Fault Injection Testing Framework by Rahal, Manal; Ahmed, Bestoun S.; Samuelsson, Jorgen is a scholarly article available to read on EtoBox.

What is Machine Learning Data Suitability and Performance Testing Using Fault Injection Testing Framework about?

Creating resilient machine learning (ML) systems has become necessary to ensure production-ready ML systems that acquire user confidence seamlessly. The quality of the input data and the model highly influence the successful end-to-end testing in data-sensitive systems. However, the testing approaches of input data are not as systematic and are few compared to model testing. To address this gap, this paper presents the Fault Injection for Undesirable Learning in input Data (FIUL-Data) testing framework that tests the resilience of ML models to multiple intentionally-triggered data faults. Data mutators explore vulnerabilities of ML systems against the effects of different fault injections. The proposed framework is designed based on three main ideas: The mutators are not random; one data mutator is applied at an instance of time, and the selected ML models are optimized beforehand. This paper evaluates the FIUL-Data framework using data from analytical chemistry, comprising retention time measurements of anti-sense oligonucleotide. Empirical evaluation is carried out in a two-step process in which the responses of selected ML models to data mutation are analyzed individually and th

Author

Rahal, Manal; Ahmed, Bestoun S.; Samuelsson, Jorgen

Published

2023

Language

EN