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
摘要
支持机器学习（ML）的系统具有数据密集的特点，这给测试和评估带来了独特的挑战。我们概述了组合覆盖率，探讨了它在支持 ML 的系统生命周期中的应用，以及它在解决支持 ML 的系统测试和评估的关键局限性方面的潜力。
Leveraging Combinatorial Coverage in the Machine Learning Product Lifecycle
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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