当我回顾我在测试自动化领域的职业生涯时，有三个不同的时期，或者说“波浪”会浮现在我的脑海中。
TestAutomation: First Wave
The first waveis filled with some good old-fashioned vendor tools like WinRunner, Silk Test,and QTP. In my eyes, these solutions started it all and set the stage forfuture testing automation innovations like Selenium.
测试自动化：第一波
第一波是一些老式的供应商工具，如WinRunner、Silk Test和QTP。在我看来，这些解决方案开创了这一切，并为将来的自动化测试创新（如Selenium）奠定了基础。
TestAutomation: Second Wave
Selenium beganthe second wave of test automation, focusing more on developers and programmingbest practices when creating automated tests.
测试自动化：第二波
Selenium开始了测试自动化的第二次浪潮，在创建自动化测试时，更多地关注开发人员和编程最佳实践。
But truth betold–in the quiet times when they think no one is listening–you can heartesters whispering the same curses they did about vendor tools: flaky tests andmaintenance driving them crazy.
但实话实说，在他们认为没人在听的安静时期，你可以听到测试人员低声抱怨他们对供应商工具的诅咒：不稳定的测试和维护让他们发狂。
The current buzzthese days is around AI and Machine Learning. Companies are rushing to createtools they can pitch as “AI-driven.” In fact, at a recent Google conference CEOSundar Pichai opened the event by stating that “We’re moving from amobile-first to an AI-first world.”
现在的热门话题是人工智能和机器学习。各公司都在争先恐后地开发他们可以称之为“人工智能驱动”的工具。事实上，在最近的一次谷歌会议上，谷歌首席执行官Sundar Pichai宣布，“我们正在从移动优先向人工智能优先的世界迈进。”
Here are eightnewer “AI”-based tools that I think will take us to the next stage of testautomation–the Third Wave. (Also, check out the Automation Guild Onlineconference for some awesome sessions we will have on AI test automation. You'llalso get a chance to ask question to many of the vendors mentioned in this article)
下面是八个新的基于“人工智能”的工具，我认为它们将带我们进入测试自动化的下一个阶段——第三次浪潮。（另外，看看自动化协会的在线会议，我们将有一些关于人工智能测试自动化的很棒的会议。您还将有机会向本文中提到的许多供应商提出问题）
Automation Guild Conference
自动化行业协会会议
Test Automation:Third Wave Tools
Here are just afew of the “third wave” automation tools I’ve seen in the market. One of themain features of this software is that many of them are leveraging machinelearning and AI-assisted technology.
测试自动化：第三波工具
以下是我在市场上看到的一些“第三波”自动化工具。该软件的一个主要特点是，他们中的许多人正在利用机器学习和人工智能辅助技术。
- Applitools
- SauceLabs
- Testim
- Sealights
- Test.AI
- Mabl
- ReTest
- ReportPortal
To learn moreabout these solutions, be sure to check out the Automation Guild 2018 onlineconference, where many experts from these vendors will be joining us for anepic event of automation awesomeness.
要了解更多关于这些解决方案的信息，请务必查看自动化行业协会2018年在线会议，届时来自这些供应商的许多专家将与我们一起参加一个自动化令人敬畏的史诗性活动。
Applitools
Applitools wasone of the first tools in the third wave that I got my hands on, and it made mestart believing that a new way of testing was possible.
Applitools是第三次浪潮中我接触到的第一批工具之一，它让我开始相信一种新的测试方法是可能的。
When I firstheard about visual validation testing, which uses a sophisticated algorithm toout potential bugs in your application without you explicitly calling out allthe elements, I thought it must be B.S.
当我第一次听说可视化验证测试时，我认为它一定是B.S。
After speakingwith founder Adam Carmi, however, and checking out Applitools for myself, Ibecame a believer.
然而，在与创始人Adam Carmi交谈并亲自查看Applitools之后，我成了一名信徒。
I discoveredthere really are no visual processing settings, percentages or configurationsthat need to be set up to create visual testing with Applitools.
我发现不需要设置可视化处理设置、百分比或配置来使用Applitools创建可视化测试。
The algorithm isentirely adaptive, and I can only imagine where they’ll take the technology asAI and machine learning advances even further.
这个算法是完全自适应的，我只能想象随着人工智能和机器学习的进一步发展，他们会把这项技术带到哪里
I recently sawan Applitools demo, and on their roadmap were some cool features they plan onadding on top of their existing machine-learning technology.
我最近看到了一个Applitools的演示，在他们的路线图上有一些很酷的特性，他们计划在现有机器学习技术的基础上添加这些特性。
Possible AIType Features:
- 可能的人工智能类型特征：
- Leveraging ML/AI-based for automated maintenance (being able togroup together similar groups of changes from different pages/browsers/devices)
- 利用基于机器学习/人工智能的自动化维护（能够将来自不同页面/浏览器/设备的类似更改组组合在一起）
- Modifying their comparison algorithms to be able to discern whatchanges are meaningful/noticeable
- 修改它们的比较算法，以便能够识别哪些变化是有意义/显著的
- Being able to automatically understand which changes are more likelyto be bugs vs. desired changes and prioritize diffs
- 能够自动理解哪些更改更可能是bug而不是期望的更改，并对差异进行优先级排序
A lot of these things are too early to see in action, butlooking at the roadmap will give you a sense of how much AI is now beingincorporated into test tool company’s roadmaps.
这些事情中的很多还为时过早，但看看路线图会让你感觉到，现在有多少人工智能正在被纳入测试工具公司的路线图中。
Sauce Labs
Of course, SauceLabs were one of the first players in the cloud-based test automation space,but with all the data they currently have access to they’re in a great positionto leverage machine learning and come up with some cool insights.
当然，Sauce实验室是基于云的测试自动化领域的第一批参与者之一，但由于他们目前可以访问的所有数据，他们在利用机器学习和提出一些很酷的见解方面处于有利地位。
That was one ofthe points that came up during the 2017 SauceCon conference. During thekeynote, CEO Charles Ramsey displayed a slide that showed how we've gone frommainframe all the way to iOT, as well as things like artificial intelligence,machine learning, and deep learning.
这是2017年索塞孔会议上提出的观点之一。在主题演讲中，首席执行官Charles Ramsey展示了一张幻灯片，展示了我们如何从大型机一路走向物联网，以及人工智能、机器学习和深度学习等。
It’s obviousthat Charles believes the use of known pattern matching and different AItechnologies can be powerful within testing.
很明显，Charles相信在测试中使用已知的模式匹配和不同的人工智能技术是非常有效的。
Which got methinking — with Sauce Labs running over a million and a half tests a day, theyhave a virtual treasure trove of data that can be used to help their customersbecome better testers.
这让我开始思考——由于Sauce实验室每天要进行一百五十万次测试，他们拥有一个虚拟的数据宝库，可以用来帮助他们的客户成为更好的测试人员。
I definitelyforesee Sauce adding more intelligence into their analytics that willproactively help customers improve their test automation.
我肯定预见Sauce会在他们的分析中加入更多的智能，这将主动帮助客户改进他们的测试自动化。
Testim
Testim tries toleverage machine learning to speed up the authoring, execution and most importantlythe maintenance of automated tests. Their goal is to help you to start trustingyour tests.
Testim试图利用机器学习来加速自动测试的编写、执行和最重要的维护。他们的目标是帮助你开始信任你的测试。
Testim focuseson reducing your flaky tests and test maintenance, which they see as one of themost significant challenges for most organizations.
Testim专注于减少测试和测试维护，他们认为这是大多数组织面临的最重大挑战之一。
Oren Rubin,co-founder of Testim, mentioned in a recent TestTalks interview that the firm’smain goal is to help liberate test automation from the exclusive realm of developersand make it simple enough for anyone on the team to create. After speaking withOren I got a sense that Testim is well on its way to achieving that goal.
Testim的联合创始人Oren Rubin在最近的一次TestTalks采访中提到，公司的主要目标是帮助将测试自动化从开发人员的专属领域解放出来，并使之足够简单，让团队中的任何人都可以创建。在和奥伦交谈之后，我感觉到Testim正在朝着这个目标前进。
Sealights
Sealights is aCloud-based platform. We all know that developers and QA–both managers andengineers–are super busy these days using CI and CD practices, where they havefrequent releases and not enough time to test the entire application multipletimes.
Sealights是一个基于云的平台。我们都知道，现在开发人员和QA（包括经理和工程师）使用CI和CD实践非常繁忙，他们发布频繁，没有足够的时间对整个应用程序进行多次测试。
That’s one ofthe main reasons Sealights was created.
这是创造Sealights的主要原因之一。
With theirmachine learning-like technology that analyzes both your code and the teststhat run against it, it lets you know exactly what your tests are covering andwhat they're not. But when Sealights says “tests,” they don't only mean unittests; they mean any kind of test, from functional, manual, performance, youname it.
通过类似机器学习的技术来分析代码和对代码运行的测试，它可以让您确切地知道测试所覆盖的内容和不覆盖的内容。但是，当Sealights说“测试”时，它们不仅仅意味着单元测试，它们意味着你认为的任何一种测试，比如：功能测试、手动测试、性能测试等。
“Quality Risks” is even a more exciting insight they provide, as itfocuses the user's efforts on the things that matter, by letting him or herknow exactly which files/methods/lines have changed in the last build thatwasn't tested by a specific test type (or any test type). Once you know that,you can easily ensure that untested code will not reach production beforeundergoing, at the very least, a minimal validation.
“质量风险”是他们提供的更令人兴奋的洞察力，因为它将用户的精力集中在重要的事情上，让用户确切地知道在上一次构建中哪些文件/方法/行发生了更改，而这些更改没有经过特定测试类型（或任何测试类型）的测试。一旦您知道了这一点，就可以很容易地确保未经测试的代码在进行最少的验证之前不会到达生产环境。
As we movetoward CI/CD, dashboarding becomes critical.
随着我们向CI/CD的方向发展，仪表板变得至关重要。
If you're likemost companies, everything today is within your CI/CD, but often this data isnot visible or accessible for consumption by your teams.
如果您像大多数公司一样，今天的一切都在您的CI/CD中，但通常您的团队无法看到或访问这些数据。
Sealights makesit easy to create a quality dashboard that everyone will see. So for everybuild, you’ll be able to understand what was tested, what the status andcoverage were, and whether it’s improving, decreasing, or has quality holes ornot.
Sealights使创建一个人人都能看到的高质量仪表板变得容易。因此，对于每一个构建，您将能够了解测试内容、状态和覆盖范围，以及它是否在改进、减少或有质量漏洞。
Test.AI
Test.AI isbilled as a tool that will add an AI brain to Selenium and Appium. It wascreated by Jason Arbon, co-author of How Google Tests Software and the founderof appdiff. Tests are defined in a simple format similar to the BDD syntax ofCucumber, so it requires no code and no need to mess with element identifiers.
Test.AI被称为一种将人工智能大脑添加到Selenium和Appium中的工具。它是由Jason Arbon创建的，他是Google如何测试软件的合著者和appdiff的创始人。测试是以类似于Cucumber的BDD语法的简单格式定义的，因此它不需要代码，也不需要处理元素标识符。
The AIidentifies screens and elements dynamically in any app and automatically drivesyour application to execute test cases.
人工智能在任何应用程序中动态识别屏幕和元素，并自动驱动应用程序执行测试用例。
It’s smartenough to know that if an element ever changes it can adjust and identify itwith you having to make any manual changes. This tool is still in beta, but Iwas given a sneak peek of it by Jason.
如果一个元素发生了变化，它就可以调整并识别它，而您必须进行任何手动更改。这个工具还处于测试阶段，但是Jason让我偷偷看了一眼
I’m excited tolearn more about it, as well as the potential of all these test tools.
我很高兴能了解更多关于它的信息，以及所有这些测试工具的潜力。
Mabl
Mablis similarto Test.AI. Mabl started by a bunch of ex-Google employess runs functionaltests against your apps or website. In Mabl terminology, you “train” your teststo interact with your applications. When you’re done recording, your trainedtests will run at a predetermined amount of time and alert you.
Mablis类似Test.AI。Mabl由一群前Google员工创建，对你的应用程序或网站运行功能测试。在Mabl术语中，您“训练”测试以与应用程序交互。录制完成后，经过训练的测试将在预定的时间内运行并提醒您。
Their websitemakes three main promises:
他们的网站做出三个主要承诺：
- Eliminates flaky tests–like theother AI-based test automation tools, Mabl can automatically detect whetherelements of your application have changed, and dynamically updates the tests tocompensate for those changes.
- Mabl can continuously comparetest results to test history to quickly detect changes and regressions,resulting in more stable releases.
- Mabl helps identify and surfaceproblems quickly, alerting you to possible impacts before they impact yourcustomers.
- 消除不稳定的测试——与其他基于人工智能的测试自动化工具一样，Mabl可以自动检测应用程序的元素是否已更改，并动态更新测试以补偿这些更改。
- Mabl可以连续比较测试结果和测试历史，以快速检测变化和回归，从而获得更稳定的释放。
- Mabl有助于快速识别和发现问题，在问题影响到客户之前提醒您可能的影响。
ReTest
Use anartificially intelligent monkey to fully automatically test your application.That’s how ReTest markets itself.
ReT使用一个人工智能的猴子来完全自动地测试你的应用程序。这就是ReTest市场本身的方法。
ReTest claims tobe different from other test automation tools because it was built specificallywith testers in mind.
ReTest声称与其他测试自动化工具不同，因为它是专门为测试人员构建的。
It also stemsfrom an artificial intelligence research project, so it tries to bake that AIintelligence into their tool, effectively eliminating the need for their usersto possess any programming skills.
它还源于一个人工智能研究项目，因此它试图将人工智能烘焙到他们的工具中，有效地消除了用户拥有任何编程技能的需求。
They, like some ofthe other tools on this list, also avoid having to select element IDs to workwith when creating a script. ReTest also automatically takes care of waittimes.
与此列表中的其他一些工具一样，它们也避免了在创建脚本时必须选择要使用的元素ID。ReTest也会自动处理等待时间。
If you want totry out ReTest, they offer an excellent step-by-step demo post.
如果你想尝试ReTest，他们会提供一个非常好的逐步演示帖子。
ReportPortal.io
Looking for aneasy-to-install-and-use dashboard? Need to triage your automation test resultsas well as create awesome graphs?
寻找一个易于安装和使用的仪表板？需要分类您的自动化测试结果以及创建很好的图表？
Even better,what if it were free?
更好的是，如果是免费的呢？
Well, I’ve gotsomething you should check out if you’ve been looking for an automation testresults dashboard solution: ReportPortal.io.
好吧，如果您一直在寻找自动化测试结果仪表板解决方案，我有一些东西您应该检查一下：ReportPortal.io。
ReportPortaljust came our with a machine-learning algorithm to help you to analyze yourresults automatically.
ReportPortal刚刚推出了一个机器学习算法，帮助您自动分析结果。
The machinelearning algorithms use all the historical data that is already in thedashboard database for your project. That means it can analyze your latestexecution, and you can be confident about the status of your test cases.
机器学习算法使用项目仪表板数据库中已存在的所有历史数据。这意味着它可以分析您的最新执行，并且您可以对测试用例的状态充满信心。
I think theability to analyze large amounts of data is the perfect use of machine learningand see this type of approach really growing in the next few years.
我认为，分析大量数据的能力是机器学习的完美运用，并看到这种方法在未来几年内真正得到发展。
To learn morecheck out my full post on getting started with ReportPortal.io
要了解更多信息，请查看我关于开始使用ReportPortal.io的完整文章
So isAI/Machine Learning Just Hype?
那么，人工智能/机器学习只是炒作吗？
Clearly,AI/Machine Learning is the latest buzz word currently being used in the testingindustry. But is it real, or just hype?
显然，人工智能/机器学习是目前测试行业使用的最新热门词汇。但这是真的，还是只是炒作？
Only time willtell if the third wave will finally fulfill the promise of reliable,easy-to-maintain test automation for all.
只有时间才能证明第三次浪潮是否最终会实现可靠、易于维护的所有测试自动化的承诺。
Let me know whatyour experience has been with these or any other tools you consider to be partof the third wave of AI test automation.
让我知道你对这些或任何其他你认为是第三次人工智能测试自动化浪潮一部分的工具有什么经验。
Also, check outthe Automation Guild Online conference for some awesome sessions we will haveon AI test automation. You'll also get a chance to ask question to many of the vendors mentioned in this article
另外，看看自动化协会的在线会议，我们将有一些关于人工智能测试自动化的令人敬畏的会议。您还将有机会向本文中提到的许多供应商提出问题
