Machine Learning Data Suitability and Performance Testing Using Fault Injection Testing Framework

TL;DR

This paper introduces FIUL-Data, a fault injection testing framework that systematically assesses the resilience of machine learning models to data faults, enhancing end-to-end testing for data-sensitive systems.

Contribution

The paper presents a novel fault injection framework for input data in ML systems, addressing the lack of systematic data testing approaches and evaluating model resilience.

Findings

Larger training datasets improve ML model resilience.

Gradient boost outperforms support vector regression on smaller datasets.

Mean squared error effectively measures model resilience.

Abstract

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.