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
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
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.
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.
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?
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
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.
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.
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.
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 aretensors, which can be equated to vectors with one or several dimensions.
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.
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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