This paper introduces a new method related to combinatorial testing and measurement, combination frequency differencing (CFD), and illustrates the use of CFD in machine learning applications. Combinatorial coverage measures have been defined and applied to a wide range of problems, including fault location and for evaluating the adequacy of test inputs and input space models. More recently, methods applying coverage measures have been used in applications of artificial intelligence and machine learning, for explainability and for analyzing aspects of transfer learning. These methods have been developed using measures that depend on the inclusion or absence of t-tuples of values in inputs, training data, and test cases. In this paper, we extend these combinatorial coverage measures to include the frequency of occurrence of combinations. Combination frequency differencing is particularly suited to AI/ML applications, where training data sets used in learning systems are dependent on the prevalence of various attributes of elements of class and non-class sets. We illustrate the use of this method by applying it to analyzing physically unclonable functions (PUFs) for bit combinations that disproportionately influences PUF response values, and in turn provides indication of the PUF potentially being more vulnerable to model-building attacks. Additionally, it is shown that combination frequency differences provide a simple but effective algorithm for classification problems.
No abstract
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.