论文标题
机器学习的软件测试
Software Testing for Machine Learning
论文作者
论文摘要
机器学习已经在各种应用中普遍存在。不幸的是，机器学习也证明容易受到欺骗的影响，导致错误甚至致命的失败。这种情况引起了质疑机器学习的广泛使用，尤其是在安全至关重要的应用中，除非我们能够确保其正确性和可信赖性能。软件验证和测试是建立用于确保此类属性的技术，例如通过检测错误。但是，机器学习的软件测试挑战是广泛而丰富的 - 但对于解决问题至关重要。本摘要讨论了机器学习的软件测试的当前最新技术。更具体地说，它讨论了用于机器学习系统软件测试的六个关键挑战领域，研究了当前的这些挑战方法，并突出了它们的局限性。该论文提供了一个研究议程，该议程带有详细的方向，可以在促进机器学习测试的最先进方面取得进展。
Machine learning has become prevalent across a wide variety of applications. Unfortunately, machine learning has also shown to be susceptible to deception, leading to errors, and even fatal failures. This circumstance calls into question the widespread use of machine learning, especially in safety-critical applications, unless we are able to assure its correctness and trustworthiness properties. Software verification and testing are established technique for assuring such properties, for example by detecting errors. However, software testing challenges for machine learning are vast and profuse - yet critical to address. This summary talk discusses the current state-of-the-art of software testing for machine learning. More specifically, it discusses six key challenge areas for software testing of machine learning systems, examines current approaches to these challenges and highlights their limitations. The paper provides a research agenda with elaborated directions for making progress toward advancing the state-of-the-art on testing of machine learning.
