  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 then compared with each other.
The results show that the FIUL-Data framework allows the evaluation of the
resilience of ML models. In most experiments cases, ML models show higher
resilience at larger training datasets, where gradient boost performed better
than support vector regression in smaller training sets. Overall, the mean
squared error metric is useful in evaluating the resilience of models due to
its higher sensitivity to data mutation.
