Machine Learning Data Suitability and Performance Testing Using Fault Injection Testing Framework
2024 (English)In: ECBS 2023: Engineering of Computer-Based Systems / [ed] Jan Kofroň, Tiziana Margaria, Cristina Seceleanu, Springer, 2024, Vol. 14390 LNCS, p. 42-59Conference paper, Published paper (Refereed)
Abstract [en]
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.
Place, publisher, year, edition, pages
Springer, 2024. Vol. 14390 LNCS, p. 42-59
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; LNCS, volume 14390
Keywords [en]
Input output programs, Machine learning, Mean square error, Software testing, Chromatography data, Data mutation, Fault injection, Input datas, Machine learning models, Machine learning systems, Machine learning testing, Machine-learning, Mutation testing, Responsible AI, Oligonucleotides
National Category
Software Engineering Computer Sciences Computer Systems
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-97919DOI: 10.1007/978-3-031-49252-5_5Scopus ID: 2-s2.0-85180147228ISBN: 978-3-031-49251-8 (print)ISBN: 978-3-031-49252-5 (electronic)OAI: oai:DiVA.org:kau-97919DiVA, id: diva2:1824170
Conference
8th International Conference, ECBS, Västerås, Sweden, October 16–18, 2023.
2024-01-042024-01-042024-01-04Bibliographically approved