This is an article or technical note which is intended to provides an insight journey of Machine Learning Systems (MLS) testing, its evolution, current paradigm and future work. Machine Learning Models, used in critical applications such as healthcare industry, Automobile, and Air Traffic control, Share Trading etc., and failure of ML Model can lead to severe consequences in terms of loss of life or property. To remediate this, developers, scientists, and ML community around the world, must build a highly reliable test architecture for critical ML application. At the very foundation layer, any test model must satisfy the core testing attributes such as test properties and its components. This attribute comes from the software engineering, but the same cannot be applied in as-is form to the ML testing and we will tell you why.

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机器学习的变异测试框架

这是一篇文章或技术说明，旨在提供有关机器学习系统（MLS）测试，其发展，当前范例和未来工作的深刻见解。机器学习模型，用于医疗保健行业，汽车和空中交通管制，股票交易等关键应用中，而ML模型的失败可能导致生命或财产损失的严重后果。为了解决这个问题，全球的开发人员，科学家和ML社区必须为关键ML应用构建高度可靠的测试体系结构。在基础层，任何测试模型都必须满足核心测试属性，例如测试属性及其组件。此属性来自软件工程，但不能将其按原样应用于ML测试，我们将告诉您原因。