Learn how to serve and deploy machine learning models built in Python locally, on the cloud, and Kubernetes with an open-source framework.

Tommy Wyher believes that no matter how much automated testing evolves, there will always be a place for manual testing in software.

This list of testing tools and libraries includes some that will be useful for automated testing, unit testing, and a variety of other testing needs.

It’s been a busy week here for Brian and I, lots of stuff going on. We did have time to collect some links however, which I share with you now. Enjoy! 8 Essential Best Practices in Windows Azure Blog Storage: “Throughout this article I’ll discuss 8 essential best practices to support you in maintaining and controlling both cost and availability. In this article I’ll look only at the best practices for the Public Containers inside Blob Storage and in future articles we’ll talk about the other types of storage.” How to Choose Between Windows Azure Queues, Windows Azure Service Bus Queues: “The article helps you compare and contrast the respective technologies and be able to make a more informed decision about which solution best meets your needs.” Why is Windows Azure Deployment Slow Compared to Heroku?: This is a balanced look at Azure and Heroku that offers great insight into why deployment times differ. So what does “The Cloud” mean now?: A humorous, but still serious, look at what “The Cloud” means to different people. Beta of Window Phone Toolkit for Amazon Web Services released: This move, IMHO, speaks volumes about Microsoft’s commitment to interoperability. Hosting your Moodle on Windows Azure: A short post highlighting 2 projects that allow you to hose Moodle on Azure. Architecting for Failure: Why Cloud Architecture is Different (Meetup): If you are in the Boston area on Feb. 7th, this looks like it would be a good Meetup to attend. NougakuDoCompanion: A “Ruby on Rails” companion for Windows Azure: A short post that highlights a project that makes hosting RoR apps on Azure easy. Handling two known issues with Windows Azure node.js SDK 0.5.2: Workarounds for an exception while testing your node.js app in the Compute Emulator and for troubles with publishing a MongoDB-enabled node.js app to Azure. A chat with Nick Quaranto about rubygems.org internals: Q&A about what goes on inside RubyGems.org to make the magic happen. Prompts and Directories - Even Better Git (and Mercurial) with PowerShell: Scott Hanselman talks about using PowerShell to work with Git and Mercurial. The client-side templating throwdown: mustache, handlebars, dust.js, and more: There’s a ton of templating solutions out there, Veena Basavaraj discusses how LinkedIn picked dust.js as it’s client-side templating solution. Beautiful docs: A collection of well-written documentation to serve as examples to those of us who write docs. Backbone.js: A Roundup for Beginners: A collection of links useful for learning how to work with Backbone.js. Source: http://blogs.msdn.com/b/silverlining/archive/2012/02/03/pie-in-the-sky-february-3rd-2012.aspx

Make sure you're employing this list of best practices to ensure you're doing functional testing right.

Automated testing takes a serious commitment, so there are some questions and considerations you should take into account before going all in.

In this article, take a deep-dive into an exploration of AWS to build a serverless eCommerce platform.

AWS Glue interactive session eradicates the complexity of setting up the infrastructure by providing serverless interactive access to AWS Glue Jobs through Jupyter Notebooks.

Here we will see what is STLC, What is SDLC, Phases of SDLC and STLC , and the difference between STLC Vs SDLC

When you are running apps in cloud environments like AWS, some observability challenges pop up. In this podcast, learn about some of those challenges.

This is a Go Lambda function that's packaged and deployed using AWS SAM.

Rewriting apps is easy. Doing it while preserving compatibility...that's a bit hard. Doing it live in production? That's the big challenge!

This quick-start guide demonstrates how to use AWS Cloud9 IDE to help you get up and running with MemoryDB for Redis.

Are you also often uninspired when you need to think of useful test data for your unit tests? Is ‘John Doe’ your best test friend? Do not worry, Java Faker comes to the rescue! In this blog, you will learn how to generate your test data. 1. Introduction Making up test data is one of the hardest tasks when writing tests. Often you will see 123 when numbers are being used, or John Doe when a name is needed. But this also means that the test will always run with the same data. This is on the one hand a good thing because your tests needs to be stable, but on the other hand a pitty because you also want to find errors. And this is more likely when random test data is being used. Java Faker is a library based on Ruby’s faker gem and Perl’s Data::Faker library. It will generate fake data for you. There are other Java libraries for that, but in order to see which library gains popularity, a view on the GitHub stars history can be quite useful. As you can see, Java Faker is on the rise. Besides that, it is based on existing fakers in other languages. In this blog, you will learn how to use Java Faker. As usual, all sources being used in this blog are available at GitHub. 2. Add Dependency As a project to experiment with, you will create a basic Spring Boot application. Just navigate to start.spring.io and create a Spring Boot application with Java 17. Java Faker can also be used with plain Java applications of course. The only thing remaining to do, is to add the javafaker dependency to the pom. XML com.github.javafaker javafaker 1.0.2 test 3. First Test Java Faker offers many data fakers which can be used. A complete list can be found here and a demo application with some examples can be found here. In the first example you create, you create a Faker instance and from that moment on, you can choose a faker and generate data. Use the Address faker in order to generate a first name, a last name and a street name. Java @Test void addressFaker() { Faker faker = new Faker(); String firstName = faker.address().firstName(); String lastName = faker.address().lastName(); String streetName = faker.address().streetName(); System.out.println("First name: " + firstName); System.out.println("Last name: " + lastName); System.out.println("Street name: " + streetName); } The output is for example the following, but this will change on each run. Shell First name: Mika Last name: Terry Street name: Wisoky Walk 4. Locale Faker In the previous example, you noticed that English names are generated. But what if you want more locale specific names? That is also possible, but note that not every faker is available in every language. The list for your locale can be found here. Since I am interested in the Dutch locale, the previous example can be rewritten als follows (note that the address faker is available in the NL locale). The only difference is that you need to provide the locale when instantiating the Faker. Java @Test void addressNlFaker() { Faker faker = new Faker(new Locale("nl-NL")); String firstName = faker.address().firstName(); String lastName = faker.address().lastName(); String streetName = faker.address().streetName(); System.out.println("First name: " + firstName); System.out.println("Last name: " + lastName); System.out.println("Street name: " + streetName); } This returns the following output, which corresponds to Dutch names. Shell First name: Irmen Last name: Vaassen Street name: Severensweg 5. Random Strings With FakeValuesService With the FakeValuesService, you can generate several strings containing random data. In the next sections, some of the features are shown. 5.1 Random Strings With Numerify With numerify, you can generate a string containing random numbers. First, you need to create a FakeValuesService instance containing a locale and a RandomService. The method numerify will return a string where the hashes (#) are replaced with numbers. For every hash, a number is replaced. Java @Test void fakeValuesServiceNumerify() { FakeValuesService fakeValuesService = new FakeValuesService(new Locale("nl-NL"), new RandomService()); String randomNumber = fakeValuesService.numerify("number##"); System.out.println("Random number is: " + randomNumber); } The output is for example: Shell Random number is: number37 5.2 Random Strings With Letterify Similar as with numerify, letterify will allow you to replace characters in a string by means of a question mark. Java @Test void fakeValuesServiceLetterify() { FakeValuesService fakeValuesService = new FakeValuesService(new Locale("nl-NL"), new RandomService()); String randomLetters = fakeValuesService.letterify("somestring??"); System.out.println("Random letters are: " + randomLetters); } The output is for example: Shell Random letters are: somestringha 5.3 Random Strings With Bothify A combination of numerify and letterify can be achieved with bothify. With bothify, you can combine random numbers and characters. Java @Test void fakeValuesServiceBothify() { FakeValuesService fakeValuesService = new FakeValuesService(new Locale("nl-NL"), new RandomService()); String randomNumbersLetters = fakeValuesService.bothify("some string with numbers ## and letters ??"); System.out.println("Random numbers and letters are: " + randomNumbersLetters); } The output is for example: Shell Random numbers and letters are: some string with numbers 10 and letters ll 5.4 Random Strings With Regexify When all of the above is not enough, you can also generate strings based on a regular expression with regexify. The next regular expression will choose one or more characters of the list a, b, or c, followed by any whitespace character and a digit. Java @Test void fakeValuesServiceRegexify() { FakeValuesService fakeValuesService = new FakeValuesService(new Locale("nl-NL"), new RandomService()); String randomBasedRegex = fakeValuesService.regexify("[abc]+\\s\\d"); System.out.println("Random string based on a pattern: " + randomBasedRegex); } The output is for example the following. Note that it generates ‘any whitespace character’ which in this case is an end of line character. Shell Random string based on a pattern: ab 9 6. Conclusion Java Faker is an easy to use data faker generation library. It relieves you from the burden of making up test data for your tests. Moreover, it will generate other data on each run which might reveal bugs in your application. The documentation of the library could have been better, but on the other hand, the library is also easy to use, so it should not be a major problem after all.

Want a fun and engaging intro to the JUnit 5 testing ecosystem? Check out this live-coding session on a new episode of "Marco Codes."

This article will see the definition of white box testing, techniques of white box testing, types, advantages, and disadvantages of white box testing.

Java is widely used in different ways, especially by popular platforms you might often use, such as Twitter, Google, Amazon, Spotify, and YouTube.

Deploy a Serverless backend for Slack using Infrastructure-as-code (IaaC).

This article will explore a test case demonstrating how to select a date from a datepicker in Selenium Webdriver using Java.

There are three primary reasons for choosing AWS S3: affordability, speed, and reliability. Here, learn how to upload files to AWS S3 in JMeter using Groovy.