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Android Testing: how to perform unit tests

If you did not read the first part “Android Testing with Kotlin, Mockito and Espresso” I recommend you take a quick look before continuing with this, because I will mention some things that came in it.

In this second instalment, we will see unit tests in greater detail. As already said, developing unit tests and integration tests is very similar and the difference lies in the number of entities, layers and/or scenarios involved in the functionality being tested. To clarify this, we will look at examples of both.

The purpose of this post is to be much less theoretical than the previous one and get our hands dirty, so that we quickly review some good practices when implementing a series of inter-related tests and look at the main functions offered by JUnit and Mockito, at least those that we have found most useful in the teams I have worked in.

Of course, there are many valid ways to face the tests of a given functionality and I certainly do not intend to imply that this is the most correct one, but I think it is a good starting point so that each one of you can later refine your own styles.

As the title of the post says, the idea is to give you a boost to begin with, but it is up to you to research more and get even more out of it.

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Android Testing: test quickly and easily

If you are involved in the development of mobile apps, it will surely not surprise you if I tell you that for years I have met dozens of good programmers that, however, have spent little (or nothing) on testing their apps. I will not lie to you; I was one of them for a long time!

Perhaps because they are “lightning projects” and times are so tight that they do not allow more than “painting screens” as soon as possible, or perhaps because the possibility of doing manual tests is so in the palm of our hands (literally in this case) that have led us to believe testing is a kind of unnecessary luxury…

In any case, the truth is that finding apps with a good test base is not as common as it should be in a professional development environment.

The objective of this post is to make a quick introduction to the implementation of Android app oriented tests, so that any colleagues wishing to leave that group and take a step further have a small initial guide.

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AWS vs Azure vs GCP: the big three Cloud Services face to face

In a multicloud world that is dominated by the big three providers – Amazon Web Services, Google Cloud and Microsoft Azure – why choose a single Cloud provider when you can enjoy everything they offer?

Broader and more varied products are increasingly made available to us, but they all are high quality services with excellent availability, top-notch security and high performance, so they are going to allow us to meet any need we might have.

Of course, they differ in name, price, technology and how they are grouped within each platform. We are going to compare the services of the big three public clouds to see which one is better for us.

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IOTA, connecting the world

How many times have you heard about Blockchain and DLT in the past few months? A lot, right? In this post we want to give you another alternative to this type of technologies.

Although it has some of Blockchain’s features, it also has some particularities that make all the difference in certain IoT-based use cases. Today we are going to talk bout IOTA.

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Quality products without bugs? Follow these 6 keys

Many people believe that software quality should be based on testing the code delivered by each developer but we at Paradigma think this is not correct. Quality is built up from the earliest stages and should not be ignored in any phase – until the last day of the project.

If we were to ask people working for many companies that boast about the quality of their products, they probably would not know what to tell us about what they really do to work on that quality.

Paradigma not only encourages us to work with the latest technologies but also us to do so striving to achieve more quality at all times.

That is why we at the QA team have defined 6 key points that help us turn our projects into success cases.

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Give your online store a boost with Google’s Enhanced Ecommerce

Actionable data are the key to success for any online store. If you have been running an ecommerce business, you may already be aware of how important data-driven ecommerce marketing is.

In this post we are going to go over the benefits of setting up ecommerce tracking in Google Analytics. In addition, this process can also be applied to the earliest stages of e-stores. Let’s go!

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Why has Google chosen Apache Airflow to be Google Cloud’s conductor?

Apache Airflow is one of the latest open-source projects that have aroused great interest in the developer community. So much so that Google has integrated it in Google Cloud’s stack as the de facto tool for orchestrating their services.

What makes this project so special and why has it been so well received? In this post we will go over its evolution and discuss its main characteristics.

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Machine Learning made easy: an introduction to PyTorch

Deep Learning with neural networks is currently one of the most promising branches of artificial intelligence. This innovative technology is commonly used in applications such as image recognition, voice recognition and machine translation, among others.

There are several options out there in terms of technologies and libraries, Tensorflow – developed by Google – being the most widespread nowadays.

However, today we are going to focus on PyTorch, an emerging alternative that is quickly gaining traction thanks to its ease of use and other advantages, such as its native ability to run on GPUs, which allows traditionally slow processes such as model training to be accelerated. It is Facebook’s main library for deep learning applications.

Its basic elements are tensors, which can be equated to vectors with one or several dimensions.

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Java 8: How to Implement a Collector

Java Version 8 has brought about big changes for this language. Lambda expressions and streams, which provide the language with functional programming features, stand out among them. But with so many changes, it is easy to miss some details, such as the one we will go over in this post.

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From Lambda to Kappa: evolution of Big Data architectures

The emergence of the Internet, more than two decades ago, has transformed business models and, in recent years, data has gained special relevance for decision making with regards to the future of companies.

In this line, for some years now, we have heard the term Big Data more and more frequently, but do we really know what it consists of?

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