�ݺ�ߣshows by User: ChouYang

�ݺ�ߣshows by User: ChouYang / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: ChouYang / Wed, 26 Feb 2020 21:28:56 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: ChouYang The Era of Intelligent Testing /slideshow/the-era-of-intelligent-testing/229247648 brady-theeraofintelligenttesting-qaorthehighway-200226212857
Traditional testing isn't keeping pace with the speed of DevOps and Continuous Delivery, but hope is not lost. AI will be used to make testing faster and more accessible to all. Existing QA solutions were built for a world where software changed infrequently. Highly adopted tools such as Selenium, Appium, and JUnit require a specialized skillset, and require too much maintenance once you start factoring in the brittle nature of tests, and the infrastructure required to run tests at scale. But there is still hope for QA in machine intelligence. Next-generation ML tools are here to help QA keep up with the agility of modern software delivery practices. You'll learn what ML strategies are being employed to make this possible, and what the future of QA - intelligent testing - looks like. Takeaways: - An overview of the QA tooling and vendor landscape - Demystifying the buzzwords - what machine intelligence in testing means today and tomorrow - How implementing DevTestOps can help increase test coverage and improve product quality]]>
Traditional testing isn't keeping pace with the speed of DevOps and Continuous Delivery, but hope is not lost. AI will be used to make testing faster and more accessible to all. Existing QA solutions were built for a world where software changed infrequently. Highly adopted tools such as Selenium, Appium, and JUnit require a specialized skillset, and require too much maintenance once you start factoring in the brittle nature of tests, and the infrastructure required to run tests at scale. But there is still hope for QA in machine intelligence. Next-generation ML tools are here to help QA keep up with the agility of modern software delivery practices. You'll learn what ML strategies are being employed to make this possible, and what the future of QA - intelligent testing - looks like. Takeaways: - An overview of the QA tooling and vendor landscape - Demystifying the buzzwords - what machine intelligence in testing means today and tomorrow - How implementing DevTestOps can help increase test coverage and improve product quality]]> Wed, 26 Feb 2020 21:28:56 GMT /slideshow/the-era-of-intelligent-testing/229247648 ChouYang@slideshare.net(ChouYang) The Era of Intelligent Testing ChouYang Traditional testing isn't keeping pace with the speed of DevOps and Continuous Delivery, but hope is not lost. AI will be used to make testing faster and more accessible to all. Existing QA solutions were built for a world where software changed infrequently. Highly adopted tools such as Selenium, Appium, and JUnit require a specialized skillset, and require too much maintenance once you start factoring in the brittle nature of tests, and the infrastructure required to run tests at scale. But there is still hope for QA in machine intelligence. Next-generation ML tools are here to help QA keep up with the agility of modern software delivery practices. You'll learn what ML strategies are being employed to make this possible, and what the future of QA - intelligent testing - looks like. Takeaways: - An overview of the QA tooling and vendor landscape - Demystifying the buzzwords - what machine intelligence in testing means today and tomorrow - How implementing DevTestOps can help increase test coverage and improve product quality <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/brady-theeraofintelligenttesting-qaorthehighway-200226212857-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Traditional testing isn't keeping pace with the speed of DevOps and Continuous Delivery, but hope is not lost. AI will be used to make testing faster and more accessible to all. Existing QA solutions were built for a world where software changed infrequently. Highly adopted tools such as Selenium, Appium, and JUnit require a specialized skillset, and require too much maintenance once you start factoring in the brittle nature of tests, and the infrastructure required to run tests at scale. But there is still hope for QA in machine intelligence. Next-generation ML tools are here to help QA keep up with the agility of modern software delivery practices. You'll learn what ML strategies are being employed to make this possible, and what the future of QA - intelligent testing - looks like. Takeaways: - An overview of the QA tooling and vendor landscape - Demystifying the buzzwords - what machine intelligence in testing means today and tomorrow - How implementing DevTestOps can help increase test coverage and improve product quality

The Era of Intelligent Testing from mabl

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0 StarWest 2019 - End to end testing: Stupid or Legit? /slideshow/starwest-2019-end-to-end-testing-stupid-or-legit/179837353 starwest-endtoendtesting-191007201209
Automating end-to-end tests is a tricky business. Many leading practitioners over the years have advised against doing ANY end-to-end test automation. As a result of this, we often see a test automation triangle that recommends a 70/20/10% split between unit, integration, and end-to-end tests to mitigate the traditional associations of the cost of troubleshooting test failures and feedback cycles at each level. But it’s 2019… many of those risks don’t really exist anymore, and the complete end-to-end is the only thing that brings together all components of your app while focusing on the true end-user functionality and experience. The test automation triangle is changing shape. Learning outcomes: - Key pain points of end-to-end test automation and the modern technologies that have eliminated those pains - The unique benefits of end-to-end test automation - Tips to bring home for getting started with end-to-end test automation]]>
Automating end-to-end tests is a tricky business. Many leading practitioners over the years have advised against doing ANY end-to-end test automation. As a result of this, we often see a test automation triangle that recommends a 70/20/10% split between unit, integration, and end-to-end tests to mitigate the traditional associations of the cost of troubleshooting test failures and feedback cycles at each level. But it’s 2019… many of those risks don’t really exist anymore, and the complete end-to-end is the only thing that brings together all components of your app while focusing on the true end-user functionality and experience. The test automation triangle is changing shape. Learning outcomes: - Key pain points of end-to-end test automation and the modern technologies that have eliminated those pains - The unique benefits of end-to-end test automation - Tips to bring home for getting started with end-to-end test automation]]> Mon, 07 Oct 2019 20:12:09 GMT /slideshow/starwest-2019-end-to-end-testing-stupid-or-legit/179837353 ChouYang@slideshare.net(ChouYang) StarWest 2019 - End to end testing: Stupid or Legit? ChouYang Automating end-to-end tests is a tricky business. Many leading practitioners over the years have advised against doing ANY end-to-end test automation. As a result of this, we often see a test automation triangle that recommends a 70/20/10% split between unit, integration, and end-to-end tests to mitigate the traditional associations of the cost of troubleshooting test failures and feedback cycles at each level. But it’s 2019… many of those risks don’t really exist anymore, and the complete end-to-end is the only thing that brings together all components of your app while focusing on the true end-user functionality and experience. The test automation triangle is changing shape. Learning outcomes: - Key pain points of end-to-end test automation and the modern technologies that have eliminated those pains - The unique benefits of end-to-end test automation - Tips to bring home for getting started with end-to-end test automation <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/starwest-endtoendtesting-191007201209-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Automating end-to-end tests is a tricky business. Many leading practitioners over the years have advised against doing ANY end-to-end test automation. As a result of this, we often see a test automation triangle that recommends a 70/20/10% split between unit, integration, and end-to-end tests to mitigate the traditional associations of the cost of troubleshooting test failures and feedback cycles at each level. But it’s 2019… many of those risks don’t really exist anymore, and the complete end-to-end is the only thing that brings together all components of your app while focusing on the true end-user functionality and experience. The test automation triangle is changing shape. Learning outcomes: - Key pain points of end-to-end test automation and the modern technologies that have eliminated those pains - The unique benefits of end-to-end test automation - Tips to bring home for getting started with end-to-end test automation

StarWest 2019 - End to end testing: Stupid or Legit? from mabl

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0 https://cdn.slidesharecdn.com/profile-photo-ChouYang-48x48.jpg?cb=1582752488 The leading SaaS provider for ML-based test automation. Enabling continuous testing with a script-free test automation framework & cutting-edge cloud services. mabl.com https://cdn.slidesharecdn.com/ss_thumbnails/brady-theeraofintelligenttesting-qaorthehighway-200226212857-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/the-era-of-intelligent-testing/229247648 The Era of Intelligent... https://cdn.slidesharecdn.com/ss_thumbnails/starwest-endtoendtesting-191007201209-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/starwest-2019-end-to-end-testing-stupid-or-legit/179837353 StarWest 2019 - End to...