A good system test consists of a set of valid scenarios. These scenarios should also cover exceptions, for example, when a user returns a product to a webshop.
To make testing effective, the data used for testing must be
accurate and realistic. But real-world data is often off-limits or subject to
privacy concerns.
Realistic Data Representation
Many people automatically associate synthetic data
with fake data, and while the term may sound misleading, it can be used to
create a dataset that is both accurate and useful. In fact, synthetic data has
been around in one form or another for decades. It's found in flight simulators
and scientific simulations of everything from atoms to galaxies.
Synthetic data is artificially created and manipulated to
meet specific usage goals, such as validating software or evaluating
performance. It can be partially or fully synthetic, meaning it contains real
data from existing observations and measurements or generated entirely from
models and simulations.
In addition to being a convenient shortcut when real data is
too hard or expensive to collect, synthetic data can be used to explore unique
scenarios that would be difficult to examine using only real-world data. For
example, it can be used to generate fraud data that would be very difficult or
dangerous to gather in the real world or train self-driving cars to handle rare
accidents, like a car crash in the middle of a populated shopping mall.
Scalability
A scalability test allows you to find the threshold for the
amount of load that an application can handle before it starts to slow down or
break down. This allows you to make necessary changes before you start adding
actual users. This prevents costly delays in production and helps you avoid
losing user confidence, sales, and revenue.
Scalability testing is a critical part of the software
development process. It is important to test the system under a range of load
conditions, both up and down. It is also essential to determine whether the
system can recover from a failure, as well as its peak performance under a
certain load.
A successful scalability test can be the difference between
an e-commerce website that fails to meet business demands during a busy day and
one that thrives. A scalability test can also save companies from having to pay
for expensive upgrades to their hardware or infrastructure in order to support
new user growth.
Data Reusability
The notion of data reusability is influenced by the way in
which it’s collected and shared. For example, if data is collected for one study
and then used in another study that asks the same question, this would usually
be considered a reuse. Conversely, when a research team retrieves its own data
from a repository to deploy in a different project, this is often not
considered a reuse because it’s essentially the same process as conducting the
original research.
In addition, the format of the data influences its
reusability. For example, astronomy datasets are released in standard formats
such as FITS, which allows them to be analyzed with existing tools and combined
with other data sets.
Successful planning for reusability involves limiting
fragmentation by implementing a next-gen data management strategy that includes
metadata collection, standardized data formats, and lineage, as well as providing
flexible, cloud-ready infrastructure to store data at scale. This is important
to enable digital transformation, operational resilience, and the creation of
additional value for customers, stakeholders and employees.
Privacy
As artificial intelligence and machine learning become more
widely used, there are growing concerns about how the algorithms that make them
work could reveal personal details or discriminate against people. This means
that researchers and developers must test their systems on real data, but it
may be difficult or impossible to get access to the kind of detailed
information they need.
Another concern is the possibility of bias — whether it’s a
subtle social bias that affects results, or more serious errors caused by
limited sample sizes or incomplete data sets. Using synthetic data can reduce
both of these issues, ensuring that the algorithms are tested against full and
varied datasets without being exposed to any real-world problems that might
occur in production.
AI-powered synthetic data generation
can be generated faster, cheaper, and in a secure environment. This type of
data can be a powerful tool for ensuring maximum software quality and privacy
compliance. It’s important to choose a data generator that retains the data
structures and referential integrity of the source data, while adding a level
of sophistication that ensures that synthetic data is completely accurate and a
true representation of real-world data.
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