Test Automation Best Practices
Designing tests and test data is the most crucial and time-consuming portion
of the testing process. To be valid, test design must be precise in indicating
the software functionalities to be tested. During the design phase, test
conditions are identified based on specified test requirements, effective test
modules and metrics are developed, and the anticipated behavior that will
yield valid results is determined. Automated testing performs evaluations
against manual test requirements to verify the reliability of the automated
process. The use of an automation framework to configure testing modules
characterizes automated testing. The automated framework supports the
development of automated test scripts while it also monitors and maintains
test results and related documentation. The structural framework for an
automated test suite is the structural foundation of automated testing.
Automation best focuses on identified priority factors for deployment. Manual
testing can precede automated testing to contribute test conditions and data
that test automation can use for regression and other types of testing.
Winning in Digital Innovation: Turning Scale and Legacy into Strengths
Over the past few years, disruptive forces have hit industry after industry.
Travel has been disrupted by Priceline, Expedia, TripAdvisor, and Airbnb,
transportation by Uber, and retail by Amazon and Alibaba. For established
businesses, the most disruptive threats tend to come from outside traditional
competition. New companies not only spot opportunities to create value that
many incumbents fail to see, they also tend to operate with different business
models. In fact, it’s no longer about having a level playing field. The
disruptors are playing an entirely new game. Google is a master of this new
game, converting an array of industries into advertising revenue. Amazon is
another serial disruptor with its Amazon Prime now in a two-horse race with
Netflix— undermining the model of traditional broadcast industries. Even those
that have not yet been significantly impacted by these forces are not safe.
Over the next five years, 40 percent of companies will face some form of
digital disruption, according to Forbes magazine. Artificial intelligence is
beginning to attack knowledge-based industries previously seen as safe from
disruption, thanks in large part to companies such as Google and Amazon
offering “AI on tap.”
How Payments Fintech Is Using Banking As A Service To Drive Growth
There are two core challenges that Banking as a Service helps an international
payments company overcome. The first is the need for a regulated entity to be
involved when it comes to offering many core banking type services such as
checking accounts or savings and lending products. The second is that the
technology requirements and capabilities to offer these products such as
maintaining account ledgers for customer accounts are very different to those
of core payments services. Obtaining the necessary regulatory licenses and
building the technology can be two of the most expensive cost items for a
financial services company. Banking as a Service exists to reduce both the
time and cost spent Fintechs spend on these two items allowing to focus on
their core businesses. And for cross-border payments companies or
Fintechs with international ambitions, a whole additional level of complexity
comes by adding a geographic dimension. Regulations and technologies are very
different country to country worldwide which means more time and more cost. We
spoke with the CEOs and senior management of various Banking as a Service
companies in the UK and US to understand what is driving the growth in Banking
as a Service.
Here’s why IT departments need predictive analytics
AI-based detection platforms are capable of monitoring IT systems in
real-time, checking for early signs of potential failures. To take one
example, my company Appnomic has managed to handle 250,000 severe IT incidents
for our clients with AI, which equals more than 850,000 man-hours of work. By
harnessing machine learning, such platforms can use past data to learn how
problems typically develop, enabling a company to step in before anything
unfortunate occurs. In 2017, Gartner coined the term “artificial intelligence
systems for IT operations” (AIOps) to describe this kind of AI-driven
predictive analysis, and the market research firm believes that the use of
AIOps will grow considerably over the next few years. In 2018, only 5 percent
of large enterprises are using AIOps, but the firm estimates that by 2023 this
figure is set to rise to 30 percent. This growth will be driven by the fact
that several benefits come from the application of machine learning and data
science to IT systems. Aside from detecting likely problems before they occur,
AI can significantly reduce false alarms, in that it can gain a more reliable
grasp of what actually leads to failures than previous technologies and human
operators.
The Garmin Hack Was a Warning
Recent victims include not just Garmin but Travelex, an international currency
exchange company, which ransomware hackers successfully hit on New Year’s Eve
last year. Cloud service provider Blackbaud—relatively low-profile, but a $3.1
billion market cap—disclosed that it paid a ransom to prevent customer data
from leaking after an attack in May. And those are just the cases that go
public. “There are certainly rather large organizations that you are not
hearing about who have been impacted,” says Kimberly Goody, senior manager of
analysis at security firm FireEye. “Maybe you don’t hear about that because
they choose to pay or because it doesn’t necessarily impact consumers in a way
it would be obvious something is wrong.” Bigger companies make attractive
ransomware targets for self-evident reasons. “They’re well-insured and can
afford to pay a lot more than your little local grocery store,” says Brett
Callow, a threat analyst at antivirus company Emsisoft. But ransomware
attackers are also opportunistic, and a poorly secured health care system or
city—neither of which can tolerate prolonged downtime—has long offered better
odds for a payday than corporations that can afford to lock things down.
Facebook’s newest proof-of-concept VR headset looks like a pair of sunglasses
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The proof-of-concept glasses aren’t just thin for looks, though — they also
apparently beam images to your eyes in a way that’s different than standard VR
headsets on the market today. I’ll let Facebook’s research team explain one of
those techniques, called “holographic optics:” Most VR displays share a common
viewing optic: a simple refractive lens composed of a thick, curved piece or
glass or plastic. We propose replacing this bulky element with holographic
optics. You may be familiar with holographic images seen at a science museum
or on your credit card, which appear to be three-dimensional with realistic
depth in or out of the page. Like these holographic images, our holographic
optics are a recording of the interaction of laser light with objects, but in
this case the object is a lens rather than a 3D scene. The result is a
dramatic reduction in thickness and weight: The holographic optic bends light
like a lens but looks like a thin, transparent sticker. The proof-of-concept
headset also uses a technique Facebook calls “polarization-based optical
folding” to help reduce the amount of space between the actual display and the
lens that focuses the image.
Regulatory Uncertainty Greatest Problem For Blockchain Entrepreneurs, Says Producer
A regulatory environment characterized by widespread uncertainty is the single biggest challenge facing entrepreneurs in the digital currency and blockchain industry, according to J.D. Seraphine, who produced the docuseries “Open Source Money.” ... The U.S. government has had an overall uneven approach to regulating digital currencies and blockchain. It is a fairly new and complex technology so part of that is attributed to a learning curve for regulators and government officials. There are also multiple agencies who have claimed jurisdiction over the regulation of digital assets each classifying them differently, making it very difficult for companies to know how to operate in this industry in the U.S. The industry needs clear regulations and rules or for the government to step back completely like they did with the early days of the internet. I believe this gray area of uncertainty is the worst thing for entrepreneurs and companies attempting to operate here, and it has led to other countries moving ahead of the U.S. in pioneering what many are calling the most important technology since the creation of the internet.
Black Hat Virtually: An Important Time to Come Together as a Community
What concerns me the most about the moment we're in right now is that the bad actors are getting more sophisticated by the day. The simple attacks don't work as often anymore. I've seen this script numerous times in the course of my career when I look at the work our research teams publish. What worked six months ago may not work now. The only way we can fight back against a more sophisticated opponent is through knowledge-sharing and collective protection, both formal and informal. I'm grateful that the Black Hat community is there to swap war stories of how we've succeeded — and failed — against adversaries. Those conversations, even digitally, will make the difference. Cybersecurity is a team sport. The conversations that the cybersecurity community will have at this year's Black Hat (and at the subsequent DEF CON) will be instrumental in shaping how we all respond going forward as the world has changed. It's our responsibility, as a security community, to take this digital conference just as seriously as we would take an in-person one.Does Ethical use of AI is the only way forward!
Companies across the world are spending a lot of time and money in AI. The
experts are doing a lot of research to Java develop high quality and extremely
useful AI-based tools. AI is surely quite popular and soon, it will turn out
to be quite popular. But, do you know why and how it should be used mostly?
Are we only looking at the ethical uses of AI? Is anyone trying to make
something nontechnical using AI as well? Sometimes, Artificial Intelligence is
considered a bit overhyped. Although, it is not. And, we have been reading
about some dangers of AI as well in the recent past. However, AI has mostly
turned out to be useful for humans, but, the fact that AI will be mimicking
human intelligence, thus, there is some bit of risk involved as well. Though
AI is most useful, it can only be considered not very useful only when humans
find it difficult to understand how to use it and make the most of it. Also,
the intentions of the people who are using have to be good. AI itself is not
harmful, but the users have to make sure that AI tools are used
rightly. Artificial Intelligence causes a bit of worry for humans too.
Enterprise architecture heats up to meet changing needs
Skills is definitely one of the biggest challenges at the moment. Most people
are making the decision to expand their EA, or start an EA if they haven't had
one, and they just move in people from one box to another. Just because you
can code software doesn't mean you can think like an architect. If you are a
systems engineer, you know the processes and systems, but it doesn't mean you
can do capability modeling and things like that. When it comes to tools, one
of the biggest barriers to EAs moving forward is ROI. The reason it's hard to
come up with an ROI is because people don't do activity-based accounting. They
don't identify how long they spend doing all of their tasks. If they had that
information, they could say, 'I can save this amount of money if I automate
these things.' The other big barrier is that people on the business side are
now tech-savvy, and they question the need for EA. They don't want EAs telling
them to use certain technology. A lot of the business [leaders] are now
thinking, 'IT is just a cost center. I want [IT] to be an order
taker.'
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