Jaganmohan Chandrasekaran, Erin Lanus, Tyler Cody, Laura J. Freeman, Raghu N. Kacker, M S Raunak, D. Richard Kuhn
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引用次数: 0
Abstract
The data-intensive nature of machine learning (ML)-enabled systems introduces unique challenges in test and evaluation. We present an overview of combinatorial coverage, exploring its applications across the ML-enabled system lifecycle and its potential to address key limitations in performing test and evaluation for ML-enabled systems.
期刊介绍：
Computer, the flagship publication of the IEEE Computer Society, publishes peer-reviewed articles written for and by computer researchers and practitioners representing the full spectrum of computing and information technology, from hardware to software and from emerging research to new applications. The aim is to provide more technical substance than trade magazines and more practical ideas than research journals. Computer seeks to deliver useful information for all computing professionals and students, including computer scientists, engineers, and practitioners of all levels.
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