Welcome to the world of automation powered by AI
歡迎來到人工智能驅動的自動化世界git
With one API call, you can add the power of AI to your mobile test automation. The team at test.ai has teamed up with Jonathan Lipps, the lead contributor of Appium and founder of Cloud Grey, to add a bit of AI to Appium. The AI finds common elements in mobile apps such as search text boxes, login buttons, etc., so test developers don’t have to worry about all those magic IDs, CSS, or XPaths. Just tell the AI what you want it to find and it will find it for you on the page — even if the element changes color, text, location, or position in the DOM. With AI, tests will be a little quicker to create and will break a little less often. Welcome to the world of automation powered by AI!github
一個API調用，你就能把人工智能的功能添加到你的手機自動化測試。 test.ai團隊已經與 Jonathan Lipps在一塊兒合做，他是Appium的領軍貢獻者和Cloud Grey的創始人，把一點人工智能添加到Appium。人工智能在手機應用查找通用元素就像在在輸入框，登陸按鈕等，因此測試開發人員不須要擔憂全部這些神奇的IDs, CSS，或者XPath。只須要告訴人工智能你想要找到什麼，它就會爲你在頁面找到——甚至元素改變了顏色，文本，位置，或者在DOM的位置。運用人工智能，測試在建立的時候速度會更快速，會更少中斷操做。歡迎來到人工智能驅動的自動化測試。web
How to Get Started
入門算法
How do you leverage the power of this AI brain in your code?bootstrap
你怎麼把人工智能的驅動能力運用到你的代碼裏呢？網絡
- Simply update your Appium project to the latest revision (see Jonathan Lipp’s Appium Pro article for the details)
- Then, find elements using a new custom AI-search strategy such as:
driver.elementByCustom(‘ai:cart’);
// This code asks the AI to find a shopping cart image on the screen for youapp
- 您只須要升級您的Appium到最新的版本（詳情能夠查看Jonathan Lipp的文章：Appium Pro article）
- :而後，用一個新的AI自定義搜索策略查找元素，好比：
driver.elementByCustom(‘ai:cart’);
// This code asks the AI to find a shopping cart image on the screen for you框架
I really can’t imagine it getting easier than this to add AI to your automation project. In fact, it is faster and simpler than the traditional methods of finding IDs, CSS or XPaths and using these more complicated search strategies.less
我真的不可思議，這操做比你直接把人工智能加到你的自動化項目裏要容易的多。實際上，這比傳統的經過ID，CSS，或者XPath或者更加複雜的查找方式查更加的簡單高效。機器學習
AI For the Planet
爲了咱們的星球
Open source is key. No team should have to find the XPath or CSS Selector of an element or beg a developer to add a magic ID for them to use in their test code. No team should have to re-invent basic AI element classifiers or re-label 100,000+ images either — what a waste of humanity to duplicate that work. Therefore, the classifier is open source. Many vendors consider this type of IP their magic sauce, but that means that most test automation engineers can’t afford it, or don’t want to integrate it into their own code base. Open source means this tech is awesomely universally accessible to all.
開源是關鍵。任何團隊都無需找XPath或者CSS元素選擇器或者乞求一個開發人員加上一個神奇的ID以幫助他們測試代碼裏可以運用。任何團隊都不須要去從新創造基礎的人工智能元素分類器或者從新標註100,000+的圖片——對人類來講複製那樣的工做就是巨大的浪費。所以，分類器是開源的。不少銷售商認爲這中IP就跟他們的神奇醬汁同樣，但那意味着絕大部分的自動化測試工程師沒法提供，或者不想要集成到他們本身的代碼基礎裏。開源工具意味着這種技術能完美一致的爲全部人使用。
Extensibility is key. This is the ‘hello world’ of bringing AI to element finding. Jonathan made sure this was a pluggable system, so any classifier can be used, or even other element search algorithms can be easily shared and plugged directly into Appium. Test.ai just open-sourced the default/reference implementation and donated to the community in the interest of sharing the power of AI with every test developer on the planet. Our mission at test.ai is to test the world’s apps. What better way is there than to help every test developer with the basics of finding elements inside of their own apps?
延展性也是重點。這是把人工智能帶到元素查找的「世界」。Jonathan 確信，這是一個可插入的系統，因此任何分類器都能被用於，或者甚至其餘的元素查找算法能被輕易的共享和直接插入到Appium。Test.ai 僅開源了默認值/參考實現，而且在共享人工智能驅動興趣社區捐贈給地球上的每個測試開發人員。咱們在Test.ai 的使命是測試世界上的應用。還有什麼比幫助每個測試研發人員從基礎定位他們本身APP內部元素更好的方式呢？
Customization is key. The testing community can improve the AI. Anyone can add new training data, alternative training methods, more rigorous relevance testing, or new labels. The AI is the property of the community, and we hope to help bootstrap every test team on the planet with a foundation of AI for their own projects. The test.ai team has shared all the training data on Kaggle, so the world can fork the data, clean it up, add it to their proprietary test frameworks, or compete to improve these classifiers. Crowdsourced, open data for AI testing systems? It is a new world.
定製化是關鍵。測試社區可以提升人工智能。任何人都能增長新的培訓數據，可選的培訓方法，更加嚴謹相關的測試，或者新標籤。人工智能是社區的資產，咱們但願能憑藉咱們人工智能的基礎幫助地球上的每個測試團隊運用到他們的項目上。test.ai 團隊已經在kaggle上分享了全部的培訓數據，因此每一個人均可以對數據進行分叉、清洗、加到本身的私有的測試框架，或者完成和改善這些分類器。爲人工智能測試系統作衆包或者開放數據？這是一個新的世界。
Reuse is key. The AI can be forked and/or re-used in other open source and proprietary frameworks. The goal at test.ai is to spread the usage of AI in all aspects of testing, in the interest of faster and smarter test automation, and ultimately better software in the world. The neural networks are based on the open source TensorFlow framework from Google. These models can be run in the cloud, locally, on mobile devices, or in a project not even thought of yet.
網絡再利用關鍵。人工智能能被分叉和/或者在其餘的開源軟件或者私有化測試框架被從新使用。test.ai的目標是把人工智能推向測試的全部層面，在更快更智能的自動化測試領域，讓世界的軟件更好。神經網絡就是基於谷歌的 TensorFlow框架。這些模型可以在運單或者本地或者手機設，甚至在任何一個未知的項目運行。
Become an AI Test Automation Engineer
成爲一我的工智能自動化測試工程師
Whether you are an AI expert, test automation geek, or just getting started with AI and testing, you can bring AI into your team and your project today. A free, open source, and single API call is all it takes. You can be the hero that brings a bit of AI to your engineering team. You can even contribute to this transformation in testing by helping add new training data or add a similar call to your favorite test framework — be an AI test automation engineer today. Ultimately, it will take our community to bring the power of AI to our entire field.
不管你是一我的工智能裝甲，自動化測試怪胎，或者指示一個剛剛開始用人工智能測試的人，從今天起你均可以把人工智能帶進你的團隊，你的項目。一個免費的開源項目，一個單獨的API是它全部的花費。你將成爲一個把人工智能帶到你的工程團隊的英雄。你甚至能爲這個測試變革作貢獻，經過幫助增長新的培訓數據或者添加一個類似的命令到你喜好的測試框架——從今天起作一我的工智能自動化測試工程師。最終，這將會帶領咱們的社區把人工智能的能量帶到全部的領域。
Seeing is believing and Jonathan Lipps created the first intro video demonstrating this working on both Android and iOS. What about web? It works there too, but it’s far less tested.
看到即相信，Jonathan Lipps創造看第一個介紹視頻，這將可以在安卓和IOS運行。那web呢？它也能在這運行，可是遠沒有測試。
This is a hello world of real AI integrating with test automation tools to make our lives just a little easier, and hopefully more fun.
這是一個讓真實的讓人工只能與自動化測試工具集成的讓咱們的生活更容易跟充滿但願和樂趣的真實世界。
By the way, it is awesome working with Jonathan Lipps — truly today’s top expert in mobile test automation. Thanks to the team of machine learning and integration engineers at test.ai for the willingness and bravery to open source something you have poured so much energy and talent into the past year. And, special thanks to our investors who thought this was a great idea when I brought it up.
順便說一下，跟Jonathan Lipps（當今在手機自動化測試的專家）一塊兒工做太棒了。感謝 test.ai 在機器學習和集成集成工程師的團隊，在過去的一年裏大家爲了開源軟件的意願和意志，傾注瞭如此巨大的能量和天賦。在此，特別感謝那些當初我帶來這個想法時認爲這是一個偉大想法的投資人。
「That’s one small step for AI, one giant leap for test automation」 .
「對於人工智能是很小的一步，對於自動化測試這是個巨大的飛躍」
— Jason Arbon CEO, test.ai
注：翻譯自https://www.test.ai/blog/welcome-to-the-world-of-automation-powered-by-ai/
龍測科技，自動化測試AI驅動，咱們在行動。
http://www.dragontesting.cn