Ping An Technology showcases cloud computing power of Chinese businesses at 2018 Cloud Expo Asia

Cloud Expo Asia, the largest and most prestigious cloud computing event in Asia, is committed to providing the latest technology and business solutions for the digital transformation of the enterprise. Attendees this year included international giants, among them, Microsoft, Amazon, Alibaba, Citrix, Singtel, Huawei, JD.com, Fujitsu and NEC as well as many innovative startups.

“Cloud computing is giving new value to emerging technologies such as artificial intelligence and blockchain,” Mr. Chan said during his speech. “What Ping An Technology is doing is democratizing technologies and working to build a better world.” The technologies developed by Ping An Technology have become a part of daily life that people rely on, ranging from payment and asset management to health management.

For example, he said, in the field of intelligent cognition, Ping An Technology has formed three technological matrices. Intelligent cognition technologies represented by facial recognition, voiceprint recognition and micro-expression have been applied in more than 200 scenarios, including community security, educational testing, social services, financial risk control, agriculture and livestock farming, and healthcare. In workplace scenarios, Ping An Technology uses facial recognition technology to help verify the identities of visitors to office buildings, promoting digital building management in communities as well as in the educational and building management sectors. Recently, based on the advanced original facial recognition technology, Ping An Technology achieved another breakthrough in its algorithms, by upping and reallocating computing power to upgrade the Ping An π intelligent door access system,that established the technology’s dominant position in the market.

In the field of medical technology, Ping An Technology has been on a fast development track. The application of disease prediction systems based on artificial intelligence and big data in Shenzhen for flu monitoring can predict the anticipated progress of the disease one week in advance, with the accuracy rate at the peak of the outbreak surpassing 90%. At present, the research and development of the model has been gradually evolving from the prediction of the spread of an illness within a group to accurate prediction for individuals. To cite an example, the model can collect data as about an individual’s living habits and medical records, and, within five minutes, deliver the prediction results for five types of cardiovascular and cerebrovascular disease.

Regarding building smart cities, Ping An Technology has entered into a collaboration with the Hong Kong Monetary Authority to enable real-time exchanges of trade and financing information through the blockchain. By doing so, both parties can gradually build and access applications around supply chain finance, credit tokenization and online payment, with the goal of ultimately jointly building an open cross-border trade financing platform.

Mr. Chan noted that Ping An Technology now focuses on the construction of an ecosystem that brings together three scenarios: finance, healthcare and the smart city. The ecosystem, with applications for China’s domestic market as the core element, will gradually cover the whole of Asia. Currently, Ping An Cloud has more than 500 million online users. All these scenarios are based on a reliable and stable Ping An Cloud. Today, Ping An Cloud has obtained nine Chinese and foreign security certifications, and is among the cloud providers with the highest security level in the financial sector. With the implementation of its cloud-oriented strategy for products and services, Ping An Technology will continue to open up more technologies and services to the outside world, and, benefiting from its own thought leadership that it has named “All in the Cloud”, focus on building an open ecology of technology and business capacities with Ping An Cloud as the core.

Mr. Fang, the company CTO and chief architect, said in his speech that cloud computing is the underlying infrastructure that Internet businesses rely on for survival and development, and that the battle for dominance in cloud computing is expected to become one of the principle areas of competition between the world’s leaders in the Internet sector. In keeping up with the latest trends, Ping An Technology has always been one step ahead. In terms of the deployment of Ping An Cloud, Ping An Technology has taken its first step, forming what it refers to as the “3+1” model covering internal, public, proprietary and private cloud solutions, and creating a beachhead in terms of applications in security compliance, autonomous control, elastic computing, virtualization and other technological areas. He noted that Ping An Cloud will, based on already established advantages and by integrating existing products and services, create a one-stop platform for availability and delivery of its products and services, with five key capabilities and the finance, healthcare and the smart city as the core target markets.

Mr. Fang described the process as “a unique journey to unlock the value of the cloud”. With cloud computing as a modus operandi sweeping across the globe, it has become the common destiny of many companies all over the world to embrace and cater to the trend. What Ping An Technology has set out to do is to be the leader and not the follower, and to unlock the huge power that cloud computing gives enterprises as the global economy reaches the next inflection point.

Ping An Technology, a wholly-owned subsidiary of Ping An Group, is committed to using AI, intelligent cognition, blockchain, cloud and other cutting-edge technologies to create a new cloud-based life for people. As a subsidiary, Ping An Technology is the high-tech core and tech business incubator within Ping An Group, and is responsible for the development and operation of the critical platforms and services for the group. As an independent entity, Ping An Technology strategizes with smart technology and manufacturing to focus on healthcare, finance, and smart city areas. Ping An Technology is dedicated to implementing the corporate philosophy of “technology changes life” by applying the international certificated technological capabilities to actual business scenarios.

More than 10,000 IT professionals and management experts form a high-level R&D force that provide expert-level technical support for the stable and reliable operation of the platform. The established cloud ecosystem has already been accessed by over 500 million internet users, and has been expanded into overseas markets, including the United States, Singapore, Hong Kong as well as other countries and regions.

SOURCE Ping An Technology

SINGAPORE, /PRNewswire/ — The development of cloud computing has dramatically expanded the development space and imagination of the network technology. An increasing number of businesses in China are willing to tell the world China’s cloud computing strategy. The 2018 Cloud Expo Asia was held in Singapore on , with the top management of more than 350 leading cloud computing providers,  including Ping An Technology CEO Ericson Chan and CTO Guowei Fang. Chan and Fang both gave speeches about cloud computing’s unique value under the trend of digitalizing.

Ping An Technology CEO Ericson Chan delivering a speech at Cloud Expo Asia

Ping An Technology CTO Guowei Fang delivering a speech at Cloud Expo Asia

Software Testing Market by Manufacturers,Types,Regions and Applications Research Report Forecast to 2025 – Newszak

Industry Overview of Software Testing Market:

The basic aim of this report is to provide a correct and strategic analysis of the Software Testing industry. The report scrutinizes each segment and sub-segments presents before you a 360-degree view of the said market.

Get a Sample PDF Report: https://www.garnerinsights.com/2018-2023-Global-Software-Testing-Market-Report-Status-and-Outlook#request-sample

The report examines the global Software Testing market with respect to the industry trends, growth rate, prospects, drivers, restraints, threats, and lucrative opportunities, by means of distinguishing the high-growth segments of the market through the various stakeholders. The statistical surveying study also elucidates the different strategies, collaborations, merger and acquisitions, product launches, innovations, and the activities in the R&D sector in the Global Software Testing Market.

Segmentation by product type:
Test Consulting And Compliance
Quality Assurance Testing
Risk And Compliance Testing Covering
Others
Segmentation by application:
Artificial Intelligence Testing
Cybersecurity Testing
Blockchain Testing
IoT Testing
Others

We can also provide the customized separate regional or country-level reports, for the following regions:
Americas
United States
Canada
Mexico
Brazil
APAC
China
Japan
Korea
Southeast Asia
India
Australia
Europe
Germany
France
UK
Italy
Russia
Spain
Middle East & Africa
Egypt
South Africa
Israel
Turkey
GCC Countries

The report also presents the market competition landscape and a corresponding detailed analysis of the major players in the market. The key players covered in this report:
Capgemini
Wipro
Cognizant
HP
Infosys
TCS
Hexaware
Katalon Studio
IBM
Tricentis Tosca Testsuite
Worksoft Certify
TestPlant eggPlant Functional

An exhaustive study has been carried out on the key players operating in the Global Software Testing Market. The report covers the revenue share, cost, product offering, recent developments, gross profit, business overview, and mergers & acquisitions, which helps the customers to understand the key players in a more profound manner.

Furthermore, the report covers the different strategies adopted by the key players operating in the market. An in-depth analysis of the market dynamics, including the drivers, restraints, challenges, threats, and the potential growth opportunities have been provided.

The Global Software Testing Market is defined by the presence of some of the leading competitors operating in the market, including the well-established players and new entrants, and the suppliers, vendors, and distributors. The key players are continuously focusing on expanding their geographic reach and broadening their customer base, in order to expand their product portfolio and come up with new advancements. The report also analyzes the development proposals and the feasibility of new investments. This report has been collated in order to provide guidance and direction to the companies and individuals interested in buying this research report.

Market dynamics:
The Software Testing Market report also shows the scope of the various commercial possibilities over the upcoming years and the positive revenue forecasts for the upcoming years. It also studies the key markets and the mentions the various regions i.e. the geographical spread of the industry.

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Essential points covered in Global Design, Research, Promotional And Consulting Services Market 2018 Research are:-

  • What will the market size and the growth rate be in 2021?
  • What are the key factors driving the global Design, Research, Promotional And Consulting Services market?
  • What are the key market trends impacting the growth of the global Design, Research, Promotional And Consulting Services market?
  • What are the challenges to market growth?
  • Who are the key vendors in the global Design, Research, Promotional And Consulting Services market?
  • What are the market opportunities and threats faced by the vendors in the global Design, Research, Promotional And Consulting Services market?
  • What are the key outcomes of the five forces analysis of the global

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How people, processes and technology determine DevOps success

In this guest post, Eran Kinsbruner, lead technical evangelist at DevOps software supplier Perfecto, talks about why success in agile software development hinges on getting the people, processes and technology elements all in alignment

Download this free guide

The human side of DevOps

DevOps practitioners often claim that taking care of the technology side of the continuous delivery equation is nothing compared to getting the people part of it right, as agile-inspired processes often require IT teams to adapt to very different ways of working. Download this guide to read best practices and real-world examples of organisations who have successfully addressed the human side of DevOps.

In a super-charged digital environment where competition is fierce and speed and quality are crucial, many organisations are incorporating DevOps practices (including continuous integration and continuous delivery) into their software development processes.

These companies know software is safer when people with complementary skills in technical operations and software development work together, not apart. However, to keep succeeding organisations must be committed to on-going self-evaluation and embrace the need to change when necessary.

The people part of the DevOps equation

For some, this means facilitating the mentoring of testers by highly qualified developers. And for others it means considering a change in software development practices to include Acceptance Test Driven Development (ATDD), which promotes defining tests as code is written.

Test automation becomes a core practice during feature development rather than afterwards. Depending on team skills, implementing Behaviour Driven Development (BDD) (which implements test automation with simple English-like syntax) serves less technical teams extremely well. There are bound to be blind spots between developer, business and test personas – and choosing development practices matched to team skills can contribute to accelerating development velocity.

Leadership is another critical aspect of success in DevOps and continuous testing. Diverse teams and personas call for strong leadership as a unifying force, and a leader’s active role in affecting change is crucial. Of course, part of leadership is to enforce stringent metrics and KPIs which help to keep everyone on track.

The importance of process

Teams must always work to clean up their code and to do it regularly. That includes more than just testing. Code refactoring (the process of restructuring computer code) is important for optimal performance, as is continually scanning for security holes.

It also includes more than just making sure production code is ‘clean’. It’s crucial to ‘treat test code as production code’ and maintain that too. Good code is always tested and version controlled.

Code can be cleaned and quality ensured in several ways. The first is through code reviews and code analysis; making make sure code is well-maintained and there are no memory leaks. Using dashboards, analytics and other visibility enablers can also help power real-time decision making which is based on concrete data – and can help teams deliver quicker and more accurately.

Finally, continuous testing by each feature team is important. Often, a team is responsible for specific functional components along with testing, and so testing code merges locally is key to detect issues earlier. Only then can teams be sure that, once a merge happens into the main branch, the entire product is not broken, and that the overall quality picture is kept consistent and visible at all times.

Let’s talk technology

When there is a mismatch between the technology and the processes or people, development teams simply won’t be able to meet their objectives

A primary technology in development is the lab itself. A test environment is the foundation of the entire testing workflow, including all continuous integration activities. It perhaps goes without saying that when the lab is not available or unstable, the entire process breaks.

For many, the requirement for speed and quality means a shift to open-source test automation tools. But, as with many free and open-source software markets, a plethora of tools complicates the selection process. Choosing an ideal framework isn’t easy, and there are material differences between the needs of developers and engineers, which must be catered for.

A developer’s primary objective is for fast feedback for their localised code changes. Frameworks like Espresso, XCUITest and JSDom or Headless Chrome Puppeteer are good options for this.

A test engineer, on the other hand, concentrates on the integration of various developers into a complete packaged product, and for that, their end-to-end testing objectives require different frameworks, like Appium, Selenium or Protractor. And production engineers are executing end-to-end tests to identify and resolve service degradations before the user experience is impacted. Frameworks such as Selenium or Protractor are also relevant here but the integration with monitoring and alerting tools becomes essential to fit into their workflow.

With such different needs, many organisations opt for a hybrid model, where they use several frameworks in tandem.

People, processes and technology – together

Ultimately, we believe that only by continually re-evaluating people, processes and technology – the three tenets of DevOps – can teams achieve accelerated delivery while ensuring quality. It’s crucial in today’s hyper-competitive landscape that speed and quality go hand in hand, and so we’d advise every organisation to take a look at their own operations and see how they can be spring-cleaned for success.

3 Ways Artificial Intelligence is Improving Software Quality

Marc Andreessen famously said that software is eating the world. This notion, that every company must become first and foremost a software company, is hardly a radical notion these days.

However, even as businesses across industries have invested deeply in their software capabilities, they are now struggling with the complexities of modern software development and deployment — software is more distributed, is released in a continuous fashion, and increasingly incorporates aspects of machine learning into the code itself, making the testing and QA function all the more challenging.

Today most enterprise labs require engineers to write testing scripts, and their technical range of skills must be equal to the developers who coded the original app. This additional overhead in quality assurance corresponds with the increasing complexity of the software itself; current methods can only be replaced by systems of increasing intelligence. Logically, AI systems will be increasingly required to test and iterate systems which themselves contain intelligence, in part because the array of input and output possibilities are bewildering.

AI in software testing is already being applied in a variety of ways. Here are three areas in which AI is making the most immediate impact:

Regression Testing

One aspect of testing that is particularly well suited for AI is regression testing, a critical part of the software lifecycle which verifies that previously tested modules continue to function predictably following code modification, serving as a safeguard that no new bugs were introduced during the most recent cycle of enhancements to the app being tested. The concept of regression testing makes it an ideal target of AI and autonomous testing algorithms because it makes use of user assertion data gathered during previous test cycles. By its very nature, regression testing itself potentially generates its own data set for future deep learning applications.

Current AI methods such as classification and clustering algorithms rely on just this type of primarily repetitive data to train models and forecast future outcomes accurately. Here’s how it works. First, a set of known inputs and verified outputs are used to set up features and train the model. Then, a portion of the dataset with known inputs and outputs are reserved for testing the model. This set of known inputs are fed to the algorithm, and the output is checked against the verified outputs to calculate accuracy of the model. If the accuracy reaches a useful threshold, then the model may be used in production.

Machine Vision

Getting computers to visualize their environment is probably the most well-known aspect of how AI is being applied in the real world. While this is most commonly understood in the context of autonomous vehicles, machine vision also has practical applications in the domain of software testing, most notably as it relates to UX and how Web pages are rendered. Determining if web pages have been correctly rendered is essential to website testing. If a layout breaks or if controls render improperly, content can become unreadable and controls can become unusable. Given the enormous range of possible designs, design components, browser variations, dynamic layout changes driven, even highly-trained human testers can be challenged to efficiently and reliably evaluate rendering correctness or recognize when rendering issues impact functionality.

AI-based machine vision is well suited to these types of tasks and can be used to capture a reviewable ‘filmstrip’ of page rendering (so no manual or automated acquisition of screen captures is required). The render is analyzed through a decision tree that segments the page into regions, then invokes a range of visual processing tools to discover, interrogate, and classify page elements.

Intelligent Test Case Generation

Defining software test cases is a foundational aspect of every software development project. However, we don’t know what we don’t know so test cases are typically limited to scenarios that have been seen before. One approach is to provide an autonomous testing solution with a test case written in a natural language and it will autonomously create the test scripts, test cases, and test data.

Among the diverse techniques under exploration today, artificial neural networks show greatest potential for adapting big datasets to regression test plan design. Multi-layered neural networks are now trained with the software application under test, at first using test data which conform to the specification, but as cycles of testing continue, the accrued data expands the test potential. After a number of regression test cycles, the neural network becomes a living simulated model of the application under test.

As AI becomes more deeply embedded in the next generation of software, developers and testers will need to incorporate AI technologies to ensure quality. While it may be a frightening prospect to imagine how a program could train itself to test your apps, it is as inevitable as speech recognition and natural language processing appeared to be a few years ago.

About the Author

Jon Seaton is the Director of Data Science for Functionize, providers of an autonomous software testing platform that incorporate AI and machine learning technologies to automate software development.

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Researchers use CRISPR gene editing to disrupt antibiotic-resistance in bacteria- Technology News, Firstpost

An encouraging new study from researchers at University of Colorado has shown that disrupting multiple bacterial genes at once is a successful strategy to use against deadly superbugs and emerging antibiotic-resistance in bacteria.

The study was  Communications Biology on Monday, and widens the scope of using genetic tools to address the growing antibiotic resistance problem by limiting the organism’s functioning.

The newly-discovered approach, called Controlled Hindrance of Adaptation of OrganismS (CHAOS) uses the gene-editing tool to alter multiple in bacterial cells to impair its core functioning abilities.

This cripples some of the central processes in the bacteria — the cell’s defence mechanisms being an important one.

Researchers have developed a combination of “kill switch” genes in for the approach.

When a single gene in the group is switched off, the bacteria appears to be able to cope, compensate and survive. But on tweaking a combination of 2 or more of these genes, the bacteria got weaker and more sensitive to antibiotic treatments.

Representational Image. Reuters

Representational Image. Reuters

“We saw that when we tweaked multiple gene expressions at the same time —even genes that would seemingly help the bacteria survive — the bacteria’s fitness dropped dramatically,” Peter Otoupal, lead author of the study University Press.

Using this technique doesn’t alter the genome of the bacteria itself, but how the genes are expressed by the cell.

“This method offers tremendous potential to create more effective combinatorial approaches,” Anushree Chatterjee, senior author of the study, University Press.

The researchers explain that the method could be further optimized for more efficient disruptions — something the team is pursuing in ongoing research.

“In the past, nobody really considered that it might be possible to slow down evolution,” Otoupal said.

“But like anything else, evolution has rules and we’re starting to learn how to use them to our advantage.”

Why Computer Software Firms Rate As Top Artificial Intelligence Stocks

Investors looking for top artificial intelligence stocks should focus on computer software vendors Salesforce.com (CRM), Splunk (SPLK), Zendesk (ZEN), Appian (APPN) and Twilio (TWLO), because they can move quickly to improve existing products by layering on AI tools, says an analyst.

X

William Blair analyst Bhavan Suri also includes Amazon.com (AMZN) in his list of artificial intelligence stocks. Amazon utilizes AI in consumer-facing products such as smart Echo-branded home speakers. It also uses AI in cloud computing services.

Suri estimates the value-added AI software market will grow to $15.07 billion by 2020, up from $1.56 billion last year.

“It is still early to gauge which companies can best leverage machine learning to create new revenue opportunities,” Suri said in a note to clients. “In our coverage universe we tend to favor a few application vendors, including Amazon, Appian, Salesforce, Splunk, Twilio, and Zendesk. Still, we expect AI to be an important aspect of the story for other vendors we cover, such as Alteryx (AYX), HubSpot (HUBS), and Tableau Software (DATA).”

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He says in other markets companies need to build AI products from scratch.

How AI Works

AI involves computer algorithms — software programs that aim to mimic the human ability to learn, interpret patterns, and make predictions.

“Since these technologies can be used in many different applications and industries, we expect AI to ultimately take the form of both a feature in enterprise software applications and as stand-alone products,” added Suri. “We expect AI as a feature to be more prominent, because application vendors should be able to quickly build associated capabilities directly into their existing offerings.”

While Suri follows software firms, chip makers such as Nvidia (NVDA) are also among the top AI stocks to watch.

Amid a broad sell-off in technology stocks this week, many computer software firms have taken a licking. The software sector trades at a high multiple to estimated revenue growth. That’s one financial metric analysts watch.

Salesforce.com, though, is holding up better than most enterprise software stocks. Salesforce.com still trades above its 50-day-moving average.

Splunk, a database and security software firm, has dipped below its 50-day line. So has Zendesk, a provider of customer-support software.

Salesforce.com closed up 1.4% to 135.12 on the stock market today. Amazon rose 2.5% to 1,701.45.

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