自主的端到端测试

机器学习在端到端测试中的核心优势是能够利用高度复杂的产品分析数据来识别和预测用户需求。 ML驱动的测试能够观察Web应用程序上的每个用户交互，了解用户经历的常见（和边缘）过程，并确保这些用例始终按预期运行。 如果该机器正在测试许多应用程序，那么它可以从所有这些应用程序中学习，以预期对应用程序的新更改将如何影响用户体验。 借助这些数据，机器学习驱动的测试已经可以比人类建立更好，更有意义的测试。 由ML驱动的自动化开发的测试比由人类构建的测试自动化更快，更便宜地构建和维护。 这样的测试可以带来更快（和更高质量）的部署，这对任何工程副总裁的预算都是一个福音。

原文题目：Autonomous End-to-End Tests

原文：Machine Learning's core advantage in E2E testing is being able to leverage highly complex product analytics data to identify and anticipate user needs. ML-driven testing is able to watch every single user interaction on a Web application, understand the common (and edge) journeys that users walk through, and make sure these use cases always work as expected.

If that machine is testing many applications, then it can learn from all of those applications to anticipate how new changes to an application will impact the user experience. ML-driven testing can already build better and more meaningful tests than humans thanks to this data.

The tests developed by ML-driven automation are built and maintained faster and far less-expensively than test automation built by humans. Such testing leads to much faster (and higher quality) deployments and is a boon for any VP Engineering's budget.

原文作者：Erik Fogg

原文链接：https://www.technewsworld.com/story/86939.html