Api Testing Quotes

Clean code can be read, and enhanced by a developer other than its original author. It has unit and acceptance tests. It has meaningful names. It provides one way rather than many ways for doing one thing. It has minimal dependencies, which are explicitly defined, and provides a clear and minimal API. Code should be literate since depending on the language, not all necessary information can be expressed clearly in code alone. -Dave Thomas, founder of OTI, godfather of the Eclipse strategy

Candidates who mindlessly jump into coding problems will do the same with testing problems. If we pose a problem to add test variations to modules, we don’t want them to start listing tests until we tell them to stop; we want the best tests first. An SET’s time is limited. We want candidates to take a step back and find the most efficient way to solve the problem, and the previous function definition can use some improvement. A good SET looks at a poorly defined API and turns it into something beautiful while testing it.

Simple to develop—IDEs and other developer tools are focused on building a single application. Easy to make radical changes to the application—You can change the code and the database schema, build, and deploy. Straightforward to test—The developers wrote end-to-end tests that launched the application, invoked the REST API, and tested the UI with Selenium. Straightforward to deploy—All a developer had to do was copy the WAR file to a server that had Tomcat installed. Easy to scale—FTGO ran multiple instances of the application behind a load balancer.

for several years starting in 2004, Bezos visited iRobot’s offices, participated in strategy sessions held at places like the Massachusetts Institute of Technology , and became a mentor to iRobot chief executive Colin Angle, who cofounded the company in 1990. “He recognized early on that robots were a very disruptive game-changer,’’ Angle says of Bezos. “His curiosity about our space led to a very cool period of time where I could count upon him for a unique perspective.’’ Bezos is no longer actively advising the company, but his impact on the local tech scene has only grown larger. In 2008, Bezos’ investment firm provided initial funding for Rethink Robotics, a Boston company that makes simple-to-program manufacturing robots. Four years later, Amazon paid $775 million for North Reading-based Kiva, which makes robots that transport merchandise in warehouses. Also in 2012, Amazon opened a research and software development outpost in Cambridge that has done work on consumer electronics products like the Echo, a Wi-Fi-connected speaker that responds to voice commands. Rodney Brooks, an iRobot cofounder who is now chief technology officer of Rethink, says he met Bezos at the annual TED Conference. Bezos was aware of work that Brooks, a professor emeritus at MIT, had done on robot navigation and control strategies. Helen Greiner, the third cofounder of iRobot, says she met Bezos at a different technology conference, in 2004. Shortly after that, she recruited him as an adviser to iRobot. Bezos also made an investment in the company, which was privately held at the time. “He gave me a number of memorable insights,’’ Angle says. “He said, ‘Just because you won a bet doesn’t mean it was a good bet.’ Roomba might have been lucky. He was challenging us to think hard about where we were going and how to leverage our success.’’ On visits to iRobot, Greiner recalls, “he’d shake everyone’s hand and learn their names. He got them engaged.’’ She says one of the key pieces of advice Bezos supplied was about the value of open APIs — the application programming interfaces that allow other software developers to write software that talks to a product like the Roomba, expanding its functionality. The advice was followed. (Amazon also offers a range of APIs that help developers build things for its products.) By spending time with iRobot, Bezos gave employees a sense they were on the right track. “We were all believers that robotics would be huge,’’ says former iRobot exec Tom Ryden. “But when someone like that comes along and pays attention, it’s a big deal.’’ Angle says that Bezos was an adviser “in a very formative, important moment in our history,’’ and while they discussed “ideas about what practical robots could do, and what they could be,’’ Angle doesn’t want to speculate about what, exactly, Bezos gleaned from the affiliation. But Greiner says she believes “there was learning on both sides. We already had a successful consumer product with Roomba, and he had not yet launched the Kindle. He was learning from us about successful consumer products and robotics.’’ (Unfortunately, Bezos and Amazon’s public relations department would not comment.) The relationship trailed off around 2007 as Bezos got busier — right around when Amazon launched the Kindle, Greiner says. Since then, Bezos and Amazon have stayed mum about most of their activity in the state. His Bezos Expeditions investment team is still an investor in Rethink, which earlier this month announced its second product, a $29,000, one-armed robot called Sawyer that can do precise tasks, such as testing circuit boards. The warehouse-focused Kiva Systems group has been on a hiring tear, and now employs more than 500 people, according to LinkedIn. In December, Amazon said that it had 15,000 of the squat orange Kiva robots moving around racks of merchandise in 10 of its 50 distribution centers. Greiner left iRo

Composite Simpler Than the Sum of Its Parts The API of a composite object should not be more complicated than that of any of its components.

scripting language is a programming language that provides you with the ability to write scripts that are evaluated (or interpreted) by a runtime environment called a script engine (or an interpreter). A script is a sequence of characters that is written using the syntax of a scripting language and used as the source for a program executed by an interpreter. The interpreter parses the scripts, produces intermediate code, which is an internal representation of the program, and executes the intermediate code. The interpreter stores the variables used in a script in data structures called symbol tables. Typically, unlike in a compiled programming language, the source code (called a script) in a scripting language is not compiled but is interpreted at runtime. However, scripts written in some scripting languages may be compiled into Java bytecode that can be run by the JVM. Java 6 added scripting support to the Java platform that lets a Java application execute scripts written in scripting languages such as Rhino JavaScript, Groovy, Jython, JRuby, Nashorn JavaScript, and so on. Two-way communication is supported. It also lets scripts access Java objects created by the host application. The Java runtime and a scripting language runtime can communicate and make use of each other’s features. Support for scripting languages in Java comes through the Java Scripting API. All classes and interfaces in the Java Scripting API are in the javax.script package. Using a scripting language in a Java application provides several advantages: Most scripting languages are dynamically typed, which makes it simpler to write programs. They provide a quicker way to develop and test small applications. Customization by end users is possible. A scripting language may provide domain-specific features that are not available in Java. Scripting languages have some disadvantages as well. For example, dynamic typing is good to write simpler code; however, it turns into a disadvantage when a type is interpreted incorrectly and you have to spend a lot of time debugging it. Scripting support in Java lets you take advantage of both worlds: it allows you to use the Java programming language for developing statically typed, scalable, and high-performance parts of the application and use a scripting language that fits the domain-specific needs for other parts. I will use the term script engine frequently in this book. A script engine is a software component that executes programs written in a particular scripting language. Typically, but not necessarily, a script engine is an implementation of an interpreter for a scripting language. Interpreters for several scripting languages have been implemented in Java. They expose programming interfaces so a Java program may interact with them.

Myth—DevOps Means Eliminating IT Operations, or “NoOps”: Many misinterpret DevOps as the complete elimination of the IT Operations function. However, this is rarely the case. While the nature of IT Operations work may change, it remains as important as ever. IT Operations collaborates far earlier in the software life cycle with Development, who continues to work with IT Operations long after the code has been deployed into production. Instead of IT Operations doing manual work that comes from work tickets, it enables developer productivity through APIs and self-serviced platforms that create environments, test and deploy code, monitor and display production telemetry, and so forth. By doing this, IT Operations become more like Development (as do QA and Infosec), engaged in product development, where the product is the platform that developers use to safely, quickly, and securely test, deploy, and run their IT services in production.

Fakes Should Be Tested A fake must have its own tests to ensure that it conforms to the API of its corresponding real implementation. A fake without tests might initially provide realistic behavior, but without tests, this behavior can diverge over time as the real implementation evolves.

1.   Identify your core capabilities as a business. Can you define precisely what gives your company competitive advantage? How easily can it be imitated? How do you deliver value to your customers? Evaluate your business as a set of processes and capabilities. Be clear on the definition, and break down big processes into smaller functions and services. 2.   Identify the services. Think through what the service, and the API for the service, might be. How do you make it a “black box”? In other words, how will you protect it from replication and theft? 3.   Where’s your advantage? How would you offer best-in-class commercial terms? Commercial terms include cost, speed, availability, quality, flexibility, and features. 4.   Can it be profitable? Would these commercial terms and capabilities be viable in the market? Would it be a viable profitable business for you? 5.   Test and evaluate. You have a critical and fact-based understanding of your core capabilities, their gaps, and the potential benefit (or lack thereof) of a platform. Build your agile approach to testing, learning, and building value as you go.

Therefore, the application should be refactored to use scan APIs with eventual consistency.

We have a microservice with a web API,

They start making a list: Every developer uses a common build environment. Every developer is supported by a continuous build and integration system. Everyone can run their code in production-like environments. Automated test suites are built to replace manual testing, liberating QA people to do higher value work. Architecture is decoupled to liberate feature teams, so developers can deliver value independently. All the data that teams need is put in easily consumed APIs

Everyone around here thinks features are important, because they can see them in their app, on the web page, or in the API. But no one seems to realize how important the build process is. Developers cannot be productive without a great build, integration, and test process.

The report also noted that active pharmaceutical ingredient (API) that failed purity tests had been reblended with good API until it met requirements.

The API said:'Test

Build, test, iterate. That’s what I’d needed at all three of my prior companies. Then I thought about what we’d been building at AWS—infrastructure as Application Programming Interfaces (APIs) that developers could invoke with a few lines of code, and for pennies per use. Modernizing communications for the software era seemed like a big problem, and I thought there was a way to solve it: by turning communication into APIs for software developers.

Cashier for payments and subscriptions, Echo for WebSockets, Scout for search, Passport for API authentication, Dusk for frontend testing, Socialite for social login, Horizon for monitoring queues, Nova for building admin panels, and Spark to bootstrap your SaaS.

In many organizations where automated functional testing is done at all, a common practice is to have a separate team dedicated to the production and maintenance of the test suite. As described at length in Chapter 4, “Implementing a Testing Strategy,” this is a bad idea. The most problematic outcome is that the developers don’t feel as if they own the acceptance tests. As a result, they tend not to pay attention to the failure of this stage of the deployment pipeline, which leads to it being broken for long periods of time. Acceptance tests written without developer involvement also tend to be tightly coupled to the UI and thus brittle and badly factored, because the testers don’t have any insight into the UI’s underlying design and lack the skills to create abstraction layers or run acceptance tests against a public API.

Instead of IT Operations doing manual work that comes from work tickets, it enables developer productivity through APIs and self-serviced platforms that create environments, test and deploy code, monitor and display production telemetry, and so forth.

How do we make our systems reliable, in spite of unreliable humans? The best systems combine several approaches: Design systems in a way that minimizes opportunities for error. For example, well-designed abstractions, APIs, and admin interfaces make it easy to do “the right thing” and discourage “the wrong thing.” However, if the interfaces are too restrictive people will work around them, negating their benefit, so this is a tricky balance to get right. Decouple the places where people make the most mistakes from the places where they can cause failures. In particular, provide fully featured non-production sandbox environments where people can explore and experiment safely, using real data, without affecting real users. Test thoroughly at all levels, from unit tests to whole-system integration tests and manual tests [3]. Automated testing is widely used, well understood, and especially valuable for covering corner cases that rarely arise in normal operation. Allow quick and easy recovery from human errors, to minimize the impact in the case of a failure. For example, make it fast to roll back configuration changes, roll out new code gradually (so that any unexpected bugs affect only a small subset of users), and provide tools to recompute data (in case it turns out that the old computation was incorrect). Set up detailed and clear monitoring, such as performance metrics and error rates. In other engineering disciplines this is referred to as telemetry. (Once a rocket has left the ground, telemetry is essential for tracking what is happening, and for understanding failures [14].) Monitoring can show us early warning signals and allow us to check whether any assumptions or constraints are being violated. When a problem occurs, metrics can be invaluable in diagnosing the issue. Implement good management practices and training—a complex and important aspect, and beyond the scope of this book.

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Should my Modern Turing Test for the twenty-first century be met, the implications for the global economy are profound. Many of the ingredients are in place. Image generation is well advanced, and the ability to write and work with the kinds of APIs that banks and websites and manufacturers would demand is in process. That an AI can write messages or run marketing campaigns, all activities that happen within the confines of a browser, seems pretty clear. Already the most sophisticated services can do elements of this. Think of them as proto–to-do lists that do themselves, enabling the automation of a wide range of tasks. We’ll come to robots later, but the truth is that for a vast range of tasks in the world economy today all you need is access to a computer; most of global GDP is mediated in some way through screen-based interfaces amenable to an AI. The challenge is in advancing what AI developers call hierarchical planning, stitching multiple goals and subgoals and capabilities into a seamless process toward a singular end. Once this is achieved, it adds up to a highly capable AI, plugged into a business or organization and all its local history and needs, that can lobby, sell, manufacture, hire, plan—everything a company can do, only with a small team of human AI managers who oversee, double-check, implement, and co-CEO with the AI.

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