Automated Testing Quotes

If it hurts, do it more frequently, and bring the pain forward.

Automated testing is a safety net that protects the program from its programmers

Developers should be able to run all automated tests on their workstations in order to triage and fix defects.

The earlier you catch defects, the cheaper they are to fix.

In software, when something is painful, the way to reduce the pain is to do it more frequently, not less.

Indeed, there is a school of thought that any work on a branch is, in the lean sense, waste—inventory that is not being pulled into the finished product.

Captcha (for Completely Automated Public Turing Test to Tell Computers and Humans Apart). Five

Asking experts to do boring and repetitive, and yet technically demanding tasks is the most certain way of ensuring human error that we can think of, short of sleep deprivation, or inebriation.

There should be two tasks for a human being to perform to deploy software into a development, test, or production environment: to pick the version and environment and to press the “deploy” button.

automated voices and the bells from the row of testing machines in the back. The walls were white cinder block, the floors speckled linoleum. At the front desk were four large black ladies. Leigh Anne handed all the documents over to one of them, who took one look at them and said in a slow drawl, “Uh-uh. This school

If you have bad tests, automation can help you do bad testing faster.

The cost of automating acceptance tests is so small in comparison to the cost of executing manual test plans that it makes no economic sense to write scripts for humans to execute.

Releasing software is too often an art; it should be an engineering discipline.

No bug is considered properly fixed without an automated regression test.

These pillars are: Attention, which amplifies the information we focus on. Active engagement, an algorithm also called “curiosity,” which encourages our brain to ceaselessly test new hypotheses. Error feedback, which compares our predictions with reality and corrects our models of the world. Consolidation, which renders what we have learned fully automated and involves sleep as a key component

He was doing well too: junior quality control at Dimple Robotics, testing the Empathy Module in the automated Customer Fulfillment models. People didn’t just want their groceries bagged, he used to explain to Charmaine: they wanted a total shopping experience, and that included a smile. Smiles were hard; they could turn into grimaces or leers, but if you got a smile right, they’d spend extra for it. Amazing to remember, now, what people would once spend extra for.

Robotics, however, is much more difficult. It requires a delicate interplay of mechanical engineering, perception AI, and fine-motor manipulation. These are all solvable problems, but not at nearly the speed at which pure software is being built to handle white-collar cognitive tasks. Once that robot is built, it must also be tested, sold, shipped, installed, and maintained on-site. Adjustments to the robot’s underlying algorithms can sometimes be made remotely, but any mechanical hiccups require hands-on work with the machine. All these frictions will slow down the pace of robotic automation.

So, when should you think about automating a process? The simplest answer is, “When you have to do it a second time.” The third time you do something, it should be done using an automated process. This fine-grained incremental approach rapidly creates a system for automating the repeated parts of your development, build, test, and deployment process.

Attention, which amplifies the information we focus on. Active engagement, an algorithm also called “curiosity,” which encourages our brain to ceaselessly test new hypotheses. Error feedback, which compares our predictions with reality and corrects our models of the world. Consolidation, which renders what we have learned fully automated and involves sleep as a key component

Why does Joe Normie think it’s a litmus test for morality if one returns one’s shopping cart? Big-box stores put out of business local retailers, they automated their systems to reduce employees, and they got customers to be their own cashiers without getting paid for their labor, and yet to prove I’m a good person, I’m supposed to do more unpaid work for them to streamline their operation?

Every change that is made to an application’s configuration, source code, environment, or data, triggers the creation of a new instance of the pipeline. One of the first steps in the pipeline is to create binaries and installers. The rest of the pipeline runs a series of tests on the binaries to prove that they can be released. Each test that the release candidate passes gives us more confidence that this particular combination of binary code, configuration information, environment, and data will work. If the release candidate passes all the tests, it can be released. The deployment pipeline has its foundations in the process of continuous integration and is in essence the principle of continuous integration taken to its logical conclusion. The aim of the deployment pipeline is threefold. First, it makes every part of the process of building, deploying, testing, and releasing software visible to everybody involved, aiding collaboration. Second, it improves feedback so that problems are identified, and so resolved, as early in the process as possible. Finally, it enables teams to deploy and release any version of their software to any environment at will through a fully automated process.

We should reinforce modern machining facilities with high performance in line with the global trend of machine industry development, press the production of products, high-speed drawings, and unmanned automation," he said. "We should set up test sites for comprehensive measurement in the factory and allow various load, interlock tests and impact tests depending on the characteristics of the products." 정품구입문의하는곳~☎위커메신저:PP444☎라인:PPPK44↔☎텔레:ppt89[☎?카톡↔rrs9] 정품구입문의하는곳~☎위커메신저:PP444☎라인:PPPK44↔☎텔레:ppt89[☎?카톡↔rrs9] 정품구입문의하는곳~☎위커메신저:PP444☎라인:PPPK44↔☎텔레:ppt89[☎?카톡↔rrs9] On the first day, Kim conducted field guidance on plants in Jagang Province, including the Kanggye Tracker General Factory, the Kanggye Precision Machinery General Factory, the Jangja Steel Manufacturing Machinery Plant and the February 8 Machine Complex. All of these factories are North Korea's leading munitions factories with decades of history. Defense ministers of South Korea, the U.S. and Japan gathered together to discuss ways to cooperate on the denuclearization of the Korean Peninsula and strengthen defense cooperation among the three countries. South Korean Defense Minister Chung Kyung-doo was acting U.S. Defense Secretary Patrick Shannahan and Japanese Defense Minister Takeshi Iwaya at the Shangri-La Hotel in Singapore, where the 18th Asia Security Conference was held from 9 a.m. on Sunday.

Automated decision-making shatters the social safety net, criminalizes the poor, intensifies discrimination, and compromises our deepest national values. It reframes shared social decisions about who we are and who we want to be as systems engineering problems. And while the most sweeping digital decision-making tools are tested in what could be called “low rights environments” where there are few expectations of political accountability and transparency, systems first designed for the poor will eventually be used on everyone.

His early research wasn’t especially original. Ax identified slight upward trends in a number of investments and tested if their average price over the previous ten, fifteen, twenty, or fifty days was predictive of future moves. It was similar to the work of other traders, often called trenders, who examine moving averages and jump on market trends, riding them until they peter out. Ax’s predictive models had potential, but they were quite crude. The trove of data Simons and others had collected proved of little use, mostly because it was riddled with errors and faulty prices. Also, Ax’s trading system wasn’t in any way automated—his trades were made by phone, twice a day, in the morning and at the end of the trading day.

Change always starts at home. The only person you can actually change is yourself. No matter how functional or dysfunctional your organization, you can begin applying XP for yourself. Anyone on the team can begin changing his own behavior. Programmers can start writing tests first. Testers can automate their tests. Customers can write stories and set clear priorities. Executives can expect transparency. Dictating practices to a team destroys trust and creates resentment. Executives can encourage team responsibility and accountability. Whether the team produces these with XP, a better waterfall, or utter chaos is up to them. Using XP, teams can produce dramatic improvements in the areas of defects, estimation, and productivity.

The cheerleaders of the new data regime rarely acknowledge the impacts of digital decision-making on poor and working-class people. This myopia is not shared by those lower on the economic hierarchy, who often see themselves as targets rather than beneficiaries of these systems. For example, one day in early 2000, I sat talking to a young mother on welfare about her experiences with technology. When our conversation turned to EBT cards, Dorothy Allen said, “They’re great. Except [Social Services] uses them as a tracking device.” I must have looked shocked, because she explained that her caseworker routinely looked at her purchase records. Poor women are the test subjects for surveillance technology, Dorothy told me. Then she added, “You should pay attention to what happens to us. You’re next.” Dorothy’s insight was prescient. The kind of invasive electronic scrutiny she described has become commonplace across the class spectrum today. Digital tracking and decision-making systems have become routine in policing, political forecasting, marketing, credit reporting, criminal sentencing, business management, finance, and the administration of public programs. As these systems developed in sophistication and reach, I started to hear them described as forces for control, manipulation, and punishment

Some of these bots are already arriving in 2021 in more primitive forms. Recently, when I was in quarantine at home in Beijing, all of my e-commerce packages and food were delivered by a robot in my apartment complex. The package would be placed on a sturdy, wheeled creature resembling R2-D2. It could wirelessly summon the elevator, navigate autonomously to my door, and then call my phone to announce its arrival, so I could take the package, after which it would return to reception. Fully autonomous door-to-door delivery vans are also being tested in Silicon Valley. By 2041, end-to-end delivery should be pervasive, with autonomous forklifts moving items in the warehouse, drones and autonomous vehicles delivering the boxes to the apartment complex, and the R2-D2 bot delivering the package to each home. Similarly, some restaurants now use robotic waiters to reduce human contact. These are not humanoid robots, but autonomous trays-on-wheels that deliver your order to your table. Robot servers today are both gimmicks and safety measures, but tomorrow they may be a normal part of table service for many restaurants, apart from the highest-end establishments or places that cater to tourists, where the human service is integral to the restaurant’s charm. Robots can be used in hotels (to clean and to deliver laundry, suitcases, and room service), offices (as receptionists, guards, and cleaning staff), stores (to clean floors and organize shelves), and information outlets (to answer questions and give directions at airports, hotels, and offices). In-home robots will go beyond the Roomba. Robots can wash dishes (not like a dishwasher, but as an autonomous machine in which you can pile all the greasy pots, utensils, and plates without removing leftover food, with all of them emerging cleaned, disinfected, dried, and organized). Robots can cook—not like a humanoid chef, but like an automated food processor connected to a self-cooking pot. Ingredients go in and the cooked dish comes out. All of these technology components exist now—and will be fine-tuned and integrated in the decade to come. So be patient. Wait for robotics to be perfected and for costs to go down. The commercial and subsequently personal applications will follow. By 2041, it’s not far-fetched to say that you may be living a lot more like the Jetsons!

Google has also benefitted from being at the inflection point of software moving from massive client-side binaries with multi-year release cycles to cloud-based services that are released every few weeks, days, or hours.1 This confluence of happy circumstances has endowed us with some similarities to the utopian software development process. Google SWEs are feature developers, responsible for building components that ship to customers. They write feature code and unit test code for those features. Google SETs are test developers, responsible for assisting SWEs with the unit test portion of their work and also in writing larger test frameworks to assist SWEs in writing small and medium tests to assess broader quality concerns. Google TEs are user developers, responsible for taking the users’ perspectives in all things that have to do with quality. From a development perspective, they create automation for user scenarios and from a product perspective, they assess the overall coverage and effectiveness of the ensemble of testing activity performed by the other engineering roles. It is not utopia, but it is our best attempt at achieving it in a practical way where real-world concerns have a way of disrupting best intentions in the most unforeseen and unforgiving way.

Without knowing it, you probably passed a variation of the Turing Test a few times today already. Yes, because a derivation of the imitation game is the “Completely Automated Public Turing test to tell Computers and Humans Apart” (CAPTCHA)

the autonomous-driving side of things, Alphabet (formerly Google), which has logged several million self-driving-car test miles, continues to lead the pack. At the end of 2016, it created a new business division, called Waymo, for its autonomous driving technology. In May 2017, Waymo and Lyft announced that they would work together on developing the technology, and later in the year, Alphabet invested $1 billion in the start-up. Others, like Cruise Automation (which GM acquired for $1 billion) and Comma.ai, which offers open-source autonomous driving technology in the same vein as Google’s Android mobile operating system, are chasing hard. Baidu, China’s leading Internet search company, has an autonomous-driving research center in Sunnyvale. Byton—backed by China’s Tencent, Foxconn, and the China Harmony New Energy auto retailer group—has an office in Mountain View, as does Didi Chuxing, the Chinese ride-sharing company in which Apple invested $1 billion. Many of these companies have taken not just inspiration but also talent from Tesla. Part of the value of an innovation cluster like Silicon Valley lies in the dispersal of intellectual labor from one node to the next. For instance, PayPal is well known in the Valley for producing a number of high performers who left the company to start, join, or invest in others. The so-called PayPal Mafia includes Reid Hoffman, who founded LinkedIn; Max Levchin, whose most recent of several start-ups is the financial services company Affirm; Peter Thiel, a Facebook board member and President Trump–supporting venture capitalist who cofounded “big data” company Palantir; Jeremy Stoppelman, who started reviews site Yelp; Keith Rabois, who was chief operating officer at Square and then joined Khosla Ventures; David Sacks, who sold Yammer to Microsoft for $1.2 billion and later became CEO at Zenefits; Jawed Karim, who cofounded YouTube; and one Elon Musk.

They got comfortable using a new tool called Bandito, developed by Prakash’s team, which allowed them to test various headline options, blurbs, and images to determine which ones would propel the story to social media virality. Bandito even went beyond Chartbeat’s headline automator in that after determining which headline package worked best, Bandito applied the insight without needing to get the human editor’s approval first.

it’s worth investing ongoing effort into a suite that is reliable. One way to achieve this is to put automated tests that are not reliable in a separate quarantine suite that is run independently.5 Or, of course, you could just delete them. If they’re version-controlled (as they should be), you can always get them back.

Having automated tests that are reliable: when the automated tests pass, teams are confident that their software is releasable.

If you are going to use automated testing and Continuous Integration (CI) to dramatically improve your productivity, you need to treat your testing investments as being at least as important, or even more important, than your development investments, which is a big cultural change for most organizations. In

To have humans executing tests that should be automated is a waste of human potential.

Developer: “Oh, we changed the way deployment works. You need to copy this new set of files over and set permission x.” Or worse, “That’s strange, let me take a look... ” followed by hours of working out what has changed and how to get it deployed. Automation

This includes the creation of automated build, integration, and test processes so that we can immediately detect when a change has been introduced that takes us out of a correctly functioning and deployable state.

MBA—$30K per year Commit to spending $2,500 per month on testing different “muses” intended to be sources of automated income. See The 4-Hour Workweek or Google “muse examples Ferriss” as a starting point.

In addition to collecting telemetry from our production services and environments, we must also collect telemetry from our deployment pipeline when important events occur, such as when our automated tests pass or fail and when we perform deployments to any environment. We

all team members as well as passers-by can see the latest information at a glance: count of automated tests, velocity, incident reports, continuous integration status, and so on. This

We cannot achieve deployments on demand if every code deployment requires two weeks to set up our test environments and data sets, and another four weeks to manually execute all our regression tests. The countermeasure is to automate our tests so we can execute deployments safely and to parallelize them so the test rate can keep up with our code development rate.

I have two complaints about the above tests. The first is that executing them manually is about as exciting as watching a banana rot, and the second is that (as I predicted), they’re not very good automated tests either.

we just don't have the time to go chasing after bugs that the automated tests could have found for us. We have to spend our time writing new code—and new bugs.

Automated testing helps developers discover their mistakes quickly (usually within minutes), which enables faster fixes as well as genuine learning-learning that is impossible when mistakes are discovered six months later during integration testing, when memories and the link between cause and effect have long faded. Instead of accruing technical debt, problems are fixed as they are found, mobilizing the entire organization if needed, because global goals outweigh local goals.

A commonly used expression is that the difference between unit tests and acceptance tests is that unit tests helps you build the thing right, whereas acceptance tests helps you build the right thing.

A key characteristic of continuous delivery is that software is always releasable. It relies on a high level of automation, including automated testing. Continuous deployment takes continuous delivery one step further in the practice of automatically deploying releasable code into production

a deployment pipeline. That’s your entire value stream from code check-in to production. That’s not an art. That’s production. You need to get everything in version control. Everything. Not just the code, but everything required to build the environment. Then you need to automate the entire environment creation process. You need a deployment pipeline where you can create test and production environments, and then deploy code into them, entirely on-demand.

a disciplined team can slow down the pace of its descent toward monolithic hell. Team members can work hard to maintain the modularity of their application. They can write comprehensive automated tests. On the other hand, they can’t avoid the issues of a large team working on a single monolithic application. Nor can they solve the problem of an increasingly obsolete technology stack. The best a team can do is delay the inevitable.

The Gausebeck-Levchin test became the first commercial application of a Completely Automated Public Turing Test to Tell Computers and Humans Apart—or CAPTCHA. Today, CAPTCHA tests are common on the internet—to be online is to be subjected to a search for a specific image—a fire hydrant or bicycle or boat—from a lineup. But at the time, PayPal was the first company to force users to prove their humanity in this fashion. Gausebeck and Levchin didn’t invent the CAPTCHA—Carnegie Mellon researchers devised something similar in 1999—but the PayPal version was the first to scale, and among the first to solve the centuries-old challenge of separating human from machine.

What is a targeted email list? How to Build an Email List? A/B testing in email marketing? What is Automated Direct Email?

QA and Acceptance Tests If QA has not already begun to write the automated acceptance tests, they should start as soon as the IPM ends. The tests for stories that are scheduled for early completion should be done early. We don’t want completed stories waiting for acceptance tests to be written.

Excellence in Statistics: Rigor Statisticians are specialists in coming to conclusions beyond your data safely—they are your best protection against fooling yourself in an uncertain world. To them, inferring something sloppily is a greater sin than leaving your mind a blank slate, so expect a good statistician to put the brakes on your exuberance. They care deeply about whether the methods applied are right for the problem and they agonize over which inferences are valid from the information at hand. The result? A perspective that helps leaders make important decisions in a risk-controlled manner. In other words, they use data to minimize the chance that you’ll come to an unwise conclusion. Excellence in Machine Learning: Performance You might be an applied machine-learning/AI engineer if your response to “I bet you couldn’t build a model that passes testing at 99.99999% accuracy” is “Watch me.” With the coding chops to build both prototypes and production systems that work and the stubborn resilience to fail every hour for several years if that’s what it takes, machine-learning specialists know that they won’t find the perfect solution in a textbook. Instead, they’ll be engaged in a marathon of trial and error. Having great intuition for how long it’ll take them to try each new option is a huge plus and is more valuable than an intimate knowledge of how the algorithms work (though it’s nice to have both). Performance means more than clearing a metric—it also means reliable, scalable, and easy-to-maintain models that perform well in production. Engineering excellence is a must. The result? A system that automates a tricky task well enough to pass your statistician’s strict testing bar and deliver the audacious performance a business leader demands. Wide Versus Deep What the previous two roles have in common is that they both provide high-effort solutions to specific problems. If the problems they tackle aren’t worth solving, you end up wasting their time and your money. A frequent lament among business leaders is, “Our data science group is useless.” And the problem usually lies in an absence of analytics expertise. Statisticians and machine-learning engineers are narrow-and-deep workers—the shape of a rabbit hole, incidentally—so it’s really important to point them at problems that deserve the effort. If your experts are carefully solving the wrong problems, your investment in data science will suffer low returns. To ensure that you can make good use of narrow-and-deep experts, you either need to be sure you already have the right problem or you need a wide-and-shallow approach to finding one.

Figure 5.1 A simple value stream map for a product

integrating infrequently, which only makes it worse. In software, when something is painful, the way to reduce the pain is to do it more frequently, not less.

If It Hurts, Do It More Frequently, and Bring the Pain Forward

Our proposal is not a technical solution but a practice: Always commit to trunk, and do it at least once a day. If this seems incompatible with making far-reaching changes to your code, then we humbly submit that perhaps you haven’t tried hard enough.

But the test happens, whether we make it formal or not. We ask and you answer. We seek a human response. But more than that—you are my test, Elefsis. Every minute I fail and imagine in my private thoughts the process for deleting you from my body and running this place with a simple automation routine which would never cover itself with flowers. Every minute I pass it, and teach you something new instead. Every minute I fail and hide things from you. Every minute I pass and show you how close we can be, with your light passing into me in a lake out of time. So close there might be no difference at all between us. Our test never ends.

Executives need to understand the basic challenges of their current architecture and work to improve it over time. The build process needs to support managing different artifacts in the system as independent entities. Additionally, a solid, maintainable test automation framework needs to be in place so developers can trust the ability to quickly localize defects in their code when it fails. Until these fundamentals are in place, you will have limited success effectively transforming your processes.

This is the worst-case scenario for automated testing: where developers start ignoring the results of the tests because they assume it is a test issue instead of a code issue.

Expect to create, architect, and maintain at least as much test code and automation scripts as you create production code. Soundly architected test code leads to soundly architected production code that is easy to understand and maintain.

It is worth emphasizing that branching by feature is really the antithesis of continuous integration, and all of our advice on how to make it work is only about ensuring that the pain isn’t too horrible come merge time.

Despite thorough testing, good documentation, and solid automation, things go wrong. Deliveries are late. Unforeseen technical problems come up.

This principle is really a statement of our aim in writing this book: Releasing software should be easy. It should be easy because you have tested every single part of the release process hundreds of times before. It should be as simple as pressing a button. The repeatability and reliability derive from two principles: automate almost everything, and keep everything you need to build, deploy, test, and release your application in version control.

Continuous integration (CI) means that whenever a developer checks in code to the source repository, a build is automatically triggered. Continuous delivery (CD) takes this one step further: after a build and automated unit tests are successful, you automatically deploy the application to an environment where you can do more in-depth testing.

The most important practice for continuous integration to work properly is frequent check-ins to trunk or mainline. You should be checking in your code at least a couple of times a day.

In some circumstances manual testing becomes more efficient than automated testing; it takes much more time to generate automated test scripts compared to running test cases manually. Especially in time-sensitive, fast-track projects, this results in a weird situation of coding around bugs instead of finding and fixing them. Project managers and QA managers should consider this issue as a project risk. They should mitigate this risk by determining the right level of test automation. Shelfware

• Automate your tests, ensuring that they do the following: – Unambiguously pass or fail – Are self-contained – Can be executed with a single click – Provide comprehensive coverage • Use branches in source control sparingly. • Automate your build process: – Build and test the software every time it changes. – Integrate static analysis into every build.

At an abstract level, a deployment pipeline is an automated manifestation of your process for getting software from version control into the hands of your users.

Thoughtful design, code review, pair programming, and a considered test strategy (including TDD practices and fully automated unit test suites) are all of the utmost importance. Techniques like assertions, defensive programming, and code coverage tools will all help minimise the likelihood of errors sneaking past.  We all know these mantras. Don’t we?

Having a good set of automated tests for your system allows you to make fundamental architectural changes with minimal risk. It gives you space to work in.

There are various incremental improvements to the way software is delivered which will yield immediate benefits, such as teaching developers to write production-ready software, running CI on production-like systems, and instituting cross-functional teams.

It is essential for the smooth running of the delivery process to fly people back and forth periodically, so that each local group has personal contact with members from other groups.

Beginner’s Guide Sixth Edition Create, Compile, and Run

Throughput is the number of transactions a system can process in a given timespan. It

i.e., TestNG”. This technique helps a beginner learn how to read the code before writing the code. The following are steps to install Selenium IDE:   Steps

Implementing functional test automation will get the team to work closer and prepare the system for the use of executable specifications later.

Algiz Technology provide enterprise it teams best MSI Packaging, Application Virtualization tools including the MSI packager, with the most advanced software packaging tools for deployment with a complete suite of automated customization, testing, MSI packaging and management reporting capabilities.

Outsourcing company providing services to mid-market companies in payroll, benefits business improvement services, enabling organizations to optimize performance and position themselves for the future.

To achieve market orientation, we won’t do a large, top-down reorganization, which often creates large amounts of disruption, fear, and paralysis. Instead, we will embed the functional engineers and skills (e.g., Ops, QA, Infosec) into each service team, or provide their capabilities to teams through automated self-service platforms that provide production-like environments, initiate automated tests, or perform deployments.

Integration problems result in a significant amount of rework to get back into a deployable state, including conflicting changes that must be manually merged or merges that break our automated or manual tests, usually requiring multiple developers to successfully resolve.

Making use of AJAX in your application design removes this potential logjam and can lead to a great end-user experience. The crux of the problem is that automated test tools are by default designed to work in synchronous fashion.

Your answer can actually communicate to the interviewer directly that you have relevant skills or experience. For example, you could say something like this: “I’m really interested in testing tools. For my senior project in college, I built an automated way of detecting certain types of errors. I learned a ton about different types of website errors and ways to detect them. I was really intrigued by how much impact automated testing can do, if done well—but I also learned just how challenging it is to do well. I’m excited to get back into this space and leverage what I learned.

Does It “Really” Need to Be an Email? By this point, you’ve probably figured out that I love email. Well, in spite of my love for email marketing, not every communication needs to be an email. In fact, there are times when emails really aren’t the best solution. So, if not email, what else? Other solutions include: In-App messages like popups, sidebars, site notifications, chat messages, browser or push notifications, desktop notifications, text messages, and even product tours and onboarding flows. Email is great when the user isn’t currently using your product. It’s great to drive them back in, but when they are right there using your product, you can’t expect them to be checking their emails at the same time. Before setting up a new email campaign, ask yourself if email is the best way to achieve your objective and drive the user behavior you seek. Maybe a popup or site notification would be more effective. Users can’t typically unsubscribe from popups, sidebars, site notifications, chat messages, or onboarding flows. They are usually better embedded into your app and more contextual. Because of this, they tend to reach users more directly than email can. That means that they can often be more effective to influence user behaviors. Push notifications, desktop notifications, and text messages still have some novelty to them. They can also reach users in different contexts from email. Although sometimes it’s better to use a different communication type, sometimes combining email with other options is the best way to go. For this reason, it’s important to consider the mix. For example, an email followed on-site by an In-App message, or an onboarding flow followed by an email summing up the process may be more effective than a single email. It will allow you to follow up on user actions, and make it really clear what needs to get done. By breaking down the steps one at a time, there’s more chances for users to learn. At LANDR, we often followed feature launch emails on-site with In-App messages. This helped to keep communications simple and goal-focused (one goal per message). The email was about getting people in the product, while the In-App message was about getting them to engage with the product. This approach allows you to evaluate and optimize each step of the process independently. Automation platforms like Intercom, ActiveCampaign and HubSpot generally allow you to combine messaging types. If your platform doesn’t currently have site messaging or onboarding functionalities, you may have to use multiple tools in conjunction in order to maximize results. This will make it trickier to track pacing, sequencing, and goals but it isn’t impossible. You also need to consider tracking effort when adding new communication types to your mix. As your program becomes more complex, it can be easy to lose track of the overall user experience: Are your users getting spammed? Are you creating a disjointed customer experience? Test things from your users’ perspective. Keep an eye out for social media messages and support requests as you do. In the next chapter we will look at setting up automations to minimize issues and maximize outcomes.

Part III describes how to accelerate flow by building the foundations of our deployment pipeline: enabling fast and effective automated testing, continuous integration, continuous delivery, and architecting for low-risk releases.

following: Create a load function that returns hardcoded

Technical debt is inherently neither good nor bad—it happens because in our daily work, we are always making trade-off decisions,” he says. “To make the date, sometimes we take shortcuts, or skip writing our automated tests, or hard-code something for a very specific case, knowing that it won’t work in the long-term. Sometimes we tolerate daily workarounds, like manually creating an environment or manually performing a deployment. We make a grave mistake when we don’t realize how much this impacts our future productivity.

Acceptance testing relies on the ability to execute automated tests in a productionlike environment. However, a vital property of such a test environment is that it is able to successfully support automated testing. Automated acceptance testing is not the same as user acceptance testing. One of the differences is that automated acceptance tests should not run in an environment that includes integration to all external systems. Instead, your acceptance testing should be focused on providing a controllable environment in which the system under test can be run. “Controllable” in this context means that you are able to create the correct initial state for our tests. Integrating with real external systems removes our ability to do this.

Sometimes I call the IRS with a question just to see how many automated phone queues they’ll pass me through before disconnecting my call. I once made it to seven hours on hold without ever talking to a human.

As a first step in test automation, it is important to learn how to build an automation framework

When the automation test pack is being designed, the most important decision is to plan the Test Scheduling of those Automated Test Scripts. The objective of test automation is to reduce the amount of time spent in Regression Testing

The best representation of automation test execution scheduling is possible through Bar Charts when multiple Automation Testers are involved in the test project.

Automation frameworks should be built in such a way that adding a new test case takes you half an hour at most.

create their own OKRs for their own organization. For example, the design department might have objectives related to moving to a responsive design; the engineering department might have objectives related to improving the scalability and performance of the architecture; and the quality department might have objectives relating to the test and release automation. The problem is that the individual members of each of these functional departments are the actual members of a cross‐functional product team. The product team has business‐related objectives (for example, to reduce the customer acquisition cost, to increase the number of daily active users, or to reduce the time to onboard a new customer), but each person on the team may have their own set of objectives that cascade down through their functional manager. Imagine if the engineers were told to spend their time on re‐platforming, the designers on moving to a responsive design, and QA on retooling. While each of these may be worthy activities, the chances of solving the business problems that the cross‐functional teams were created to solve are not high.

Automated systems can allow mistakes, errors, and attacks to be propagated and multiplied in far more damaging ways than manual systems. As the DevOps comedy account @DevOpsBorat says, “To make error is human. To propagate error to all server in automatic way is #devops.” 2 Furthermore, automated tooling is fallible; and as we know so well in the security world, it can be easy for humans to begin to trust in the computer and stop applying sense or judgment to the results. This can lead to teams trusting that if the tests pass, the system is working as expected, even if other evidence might indicate otherwise.

here are some steps to identify and track code that should be reviewed carefully: Tagging user stories for security features or business workflows which handle money or sensitive data. Grepping source code for calls to dangerous function calls like crypto functions. Scanning code review comments (if you are using a collaborative code review tool like Gerrit). Tracking code check-in to identify code that is changed often: code with a high rate of churn tends to have more defects. Reviewing bug reports and static analysis to identify problem areas in code: code with a history of bugs, or code that has high complexity and low automated test coverage. Looking out for code that has recently undergone large-scale “root canal” refactoring. While day-to-day, in-phase refactoring can do a lot to simplify code and make it easier to understand and safer to change, major refactoring or redesign work can accidentally change the trust model of an application and introduce regressions.

I read a post on Palestinian Refugee ResearchNet’s blog claiming that Facebook was blocking the term “Palestinian” from being used in page titles. The post included a screenshot of an attempt to create a page called “Palestinian Refugee ResearchNet,” with a warning splashed across the top that read: “Our automated system will not allow the name ‘Palestinian Refugee ResearchNet.’ It may violate our Pages Guidelines or contain a word or phrase that is blocked to prevent the creation of unofficial or otherwise prohibited Pages. If you believe this is an error, please contact our Customer Support team.” The blogger, Rex Brynen, tested several similar titles, replacing “Palestinian” with “Israeli” and “Afghan.” Both worked, so he wrote to the support team.

What I am telling you here is actually nothing new. So why switch from analyzing assumption-based, transparent models to analyzing assumption-free black box models? Because making all these assumptions is problematic: They are usually wrong (unless you believe that most of the world follows a Gaussian distribution), difficult to check, very inflexible and hard to automate. In many domains, assumption-based models typically have a worse predictive performance on untouched test data than black box machine learning models. This is only true for big datasets, since interpretable models with good assumptions often perform better with small datasets than black box models. The black box machine learning approach requires a lot of data to work well. With the digitization of everything, we will have ever bigger datasets and therefore the approach of machine learning becomes more attractive. We do not make assumptions, we approximate reality as close as possible (while avoiding overfitting of the training data).

The Most Widely Known Path If you're like most people, you believe landing an interview is limited to these three steps: 1.) Applying online, 2.) HR reviewing your application, and 3.) If your application is selected, the hiring manager reviewing it. You believe this because almost everything you’ve read comes from current or former HR folks. This process has significant flaws. Because the Internet made applying for positions easy, HR was drowning in applications. As a result, the HR Elimination system was born. That’s not its official name, but the name fits. The official name is Applicant Tracking System or ATS. ATS systems reject, on average, 75% of all applicants. Sometimes the rejection rate can be as high as 90%. J. P. Medved, content director at Capterra, a firm that helps companies find the right software for their business, said, Reducing the number of candidates might seem good if we're weeding out irrelevant resumes...In reality, many of these rejected candidates were knocked out of the running for bad reasons. An automated system, like an ATS, will sometimes reject people for very minor reasons, like incorrect resume formatting. Bersin & Associates, an Oakland-based firm specializing in talent management, tested an ATS system. They created the perfect resume for an ideal candidate for a clinical scientist position. Matching the resume to the job description from a leading manufacturer, they submitted the resume to an applicant tracking system. The ATS lost one of the candidate's work experiences. It also failed to read several educational degrees. As a result, the perfect resume for a clinical scientist position earned a score of 43, because the applicant tracking system misread it. Similarly, a Vice-President of Human Resources decided to test his company's ATS system. He applied for a job at his own company and received an automated rejection letter from the ATS.

Miguel Antunes, R&D Principle Software Engineer at OutSystems, a low-code platform vendor, relayed an example of this very challenge. Their Engineering Productivity team at OutSystems was five years old. The team’s mission was to help product teams run their builds efficiently, maintain infrastructure, and improve test execution. The team kept growing and took on extra responsibilities around continuous integration (CI), continuous delivery (CD), and infrastructure automation.

Without automated testing, continuous integration is the fastest way to get a big pile of junk that never compiles or runs correctly.